A content recommendation method and device, electronic equipment and storage medium

Abstract

This application relates to the field of internet technology, and more particularly to a content recommendation method, apparatus, electronic device, and storage medium for satisfying a user's broad, non-specific goal needs, thereby improving the user's goal achievement rate. The method includes: in response to a first setting operation triggered by the current browsing interface, presenting a goal setting interface, the goal setting interface including at least one preset browsing goal; in response to a second setting operation triggered by the goal setting interface, presenting goal prompt information in the goal setting interface; the goal prompt information is used to guide the user to set a browsing goal by referring to the at least one preset browsing goal; after the user sets the browsing goal, presenting a content recommendation interface, the content recommendation interface including recommended content related to the user's browsing goal.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to a content recommendation method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the popularization of the Internet, more and more people will browse information streams extensively through information browsing platforms for learning, entertainment and relaxation.

[0003] For example, in existing news and information applications (such as browsers), when an audience has no specific goal, the system typically recommends content based on their interest tags. While this approach can attract short-term attention, the recommended content is monotonous and lacks diversity, making it difficult to provide differentiated content. Conversely, when an audience has a specific goal, the system directly uses a search engine to perform a specific search based on the entered search terms, such as finding a particular product or information. While this method provides highly accurate results that closely match the search terms, it is too targeted, limiting the audience's opportunities to discover relevant but not directly matching valuable content.

[0004] Furthermore, users sometimes have broad, non-specific needs. In such cases, users have neither a clear search requirement nor the desire to view conventional recommended content. Instead, they prefer to obtain information and content in an easy, non-specific way. Because existing information feed platforms can only recommend content based on users' interest tags or specific search terms, they cannot meet users' broad, non-specific needs. This limitation not only affects the user's browsing experience but may also cause users to miss broader information while focusing excessively on recommended content, ultimately wasting time and deviating from their initial needs. Summary of the Invention

[0005] This application provides a content recommendation method, apparatus, electronic device, and storage medium to meet the broad, non-specific needs of objects, thereby improving the object's goal achievement rate.

[0006] The first content recommendation method provided in this application includes:

[0007] In response to a first setting operation triggered by the current browsing interface, a target setting interface is presented, the target setting interface including: at least one preset browsing target;

[0008] In response to a second setting operation triggered through the target setting interface, target prompt information is displayed in the target setting interface; the target prompt information is used to guide the user to browse the target by referring to the at least one preset browsing target setting.

[0009] After the user sets the browsing target, a content recommendation interface is presented, which includes recommended content related to the browsing target.

[0010] The second content recommendation method provided in this application includes:

[0011] Upon receiving a target setting request from a client, a target prompt message is sent to the client; wherein, the client is used to present the target prompt message in a target setting interface; the target setting interface is presented by the client in response to a first setting operation triggered by the current browsing interface, and the target setting interface includes: at least one preset browsing target; the target prompt message is used to guide the user to set the browsing target by referring to the at least one preset browsing target;

[0012] After the user sets the browsing target, recommended content related to the browsing target is searched, and the search results are sent to the client; wherein, the client is used to display the recommended content through a content recommendation interface.

[0013] The first content recommendation device provided in this application includes:

[0014] The first setting unit is used to present a target setting interface in response to a first setting operation triggered by the current browsing interface, the target setting interface including: at least one preset browsing target;

[0015] The second setting unit is used to respond to a second setting operation triggered through the target setting interface and to present target prompt information in the target setting interface; the target prompt information is used to guide the user to browse the target by referring to the at least one preset browsing target setting.

[0016] The content display unit is used to present a content recommendation interface after the user sets the browsing target of the object. The content recommendation interface includes recommended content related to the browsing target of the object.

[0017] The second content recommendation device provided in this application embodiment includes:

[0018] The first feedback unit is used to send target prompt information to the client after receiving a target setting request from the client; wherein, the client is used to present the target prompt information in the target setting interface; the target setting interface is presented by the client in response to a first setting operation triggered by the current browsing interface, and the target setting interface includes: at least one preset browsing target; the target prompt information is used to guide the user to set the browsing target by referring to the at least one preset browsing target;

[0019] The second feedback unit is used to search for recommended content related to the browsing target after the user sets the browsing target, and send the search results to the client; wherein the client is used to display the recommended content through a content recommendation interface.

[0020] An electronic device provided in this application includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of any of the above-described content recommendation methods.

[0021] This application provides a computer-readable storage medium including a computer program. When the computer program is run on an electronic device, the computer program is used to cause the electronic device to perform the steps of any of the above-described content recommendation methods.

[0022] This application provides a computer program product, which includes a computer program stored in a computer-readable storage medium. When a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the steps of any of the above-described content recommendation methods.

[0023] The beneficial effects of this application are as follows:

[0024] This application provides a content recommendation method, apparatus, electronic device, and storage medium. Specifically, this application provides a novel goal-setting method that allows users to explicitly express broad and non-specific goals (hereinafter referred to as broad goals), such as learning new knowledge, finding inspiration, or entertainment and relaxation. Furthermore, during the process of users setting and browsing goals, the system actively guides users through a goal-setting interface and goal prompts. This process not only simplifies the user's operation steps but also enhances the user's sense of participation in the system. When setting goals, users receive clear guidance and prompts, ensuring that their goals are both specific and meet their own needs. This interactive approach enhances the user's sense of control and satisfaction.

[0025] After setting browsing goals, the system presents a content recommendation interface with personalized recommendations related to those goals, ensuring that the recommendations highly match the user's actual needs. This process differentiates itself from traditional search engines and recommendation systems, eliminating the need for users to painstakingly search for specific keywords and avoiding the uniformity of conventional recommendations. This significantly enhances the user experience, improving recommendation accuracy and effectively meeting personalized needs, thereby significantly improving the overall user experience and engagement. This allows users to more efficiently achieve their broad browsing goals, increasing the goal attainment rate.

[0026] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0028] Figure 1 This is an optional schematic diagram of an application scenario in an embodiment of this application;

[0029] Figure 2 A flowchart illustrating the implementation of a content recommendation method provided in this application embodiment;

[0030] Figure 3 This is a schematic diagram of a current browsing interface and a target setting interface in an embodiment of this application;

[0031] Figure 4 This is a schematic diagram of a first target prompt message in an embodiment of this application;

[0032] Figure 5 This is a schematic diagram of another first target prompting message in an embodiment of this application;

[0033] Figure 6 This is a schematic diagram of a second target prompt message in an embodiment of this application;

[0034] Figure 7 This is a schematic diagram of a multi-turn dialogue in an embodiment of this application;

[0035] Figure 8 This is a schematic diagram of a content preview card in an embodiment of this application;

[0036] Figure 9 This is a schematic diagram of another content preview card in an embodiment of this application;

[0037] Figure 10 This is a schematic diagram of a content recommendation interface in an embodiment of this application;

[0038] Figure 11 This is a schematic diagram of a pop-up browsing prompt message in an embodiment of this application;

[0039] Figure 12 This is a schematic diagram of the first type of browsing prompt information in the embodiments of this application;

[0040] Figure 13 This is a schematic diagram of the second type of browsing prompt information in the embodiments of this application;

[0041] Figure 14 This is a schematic diagram of a viewing control in an embodiment of this application;

[0042] Figure 15 This is a schematic diagram of a content browsing report in an embodiment of this application;

[0043] Figure 16 This is a schematic diagram illustrating a tutorial on using small notes and a summary report in an embodiment of this application;

[0044] Figure 17 A flowchart illustrating the implementation of another content recommendation method provided in this application embodiment;

[0045] Figure 18 This is a timing diagram illustrating the interaction between a client and a server in one embodiment of this application.

[0046] Figure 19 This is a schematic diagram of the composition structure of a content recommendation device according to an embodiment of this application;

[0047] Figure 20 This is a schematic diagram of the composition structure of another content recommendation device in an embodiment of this application;

[0048] Figure 21 This is a schematic diagram of the hardware structure of an electronic device using an embodiment of this application;

[0049] Figure 22 This is a schematic diagram of the hardware structure of another electronic device using an embodiment of this application. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this application. Obviously, the described embodiments are only some embodiments of the technical solutions of this application, and not all embodiments. Based on the embodiments recorded in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the technical solutions of this application.

[0051] The following explains some terms used in the embodiments of this application:

[0052] Browsing goal: This refers to the user's intention or purpose during the browsing process. It can be set through system-provided preset options or by the user's own definition, clearly specifying the user's current browsing needs. For example, "entertainment and relaxation," "learning new knowledge," or "finding inspiration" are common browsing goals.

[0053] Recommended content refers to specific content that the system selects from a vast amount of information and presents to users based on their browsing goals, aiming to meet their immediate needs and interests. Recommended content can include articles, videos, music, learning materials, etc., depending on the user's browsing goals. For example, for the goal of "entertainment and relaxation," recommended content might include lighthearted movies, humorous short videos, or soothing music; while for the goal of "learning new knowledge," it might include relevant online courses, academic papers, or popular science articles.

[0054] In this embodiment, the recommended content not only considers the browsing goals of the user, but also dynamically adjusts it by combining real-time interaction data, historical behavior, preferences, etc., to ensure the accuracy and relevance of the recommendations and improve the user's satisfaction and browsing experience.

[0055] Content preview cards: These are used to display visual information about the content, such as a cover image, to intuitively convey the core theme and style of the content and attract the user's attention. In this embodiment, content preview cards can provide users with immediate visual feedback, helping them quickly assess the relevance of the recommended content while avoiding premature distraction.

[0056] Content summary cards: Similar to content summary cards, in addition to displaying visual information about the content (such as a cover image), they can also provide a brief text description (such as a brief title) and support links, allowing users to click on the content to enter the content details page for more information.

[0057] Content browsing report: This is a comprehensive analysis report generated based on the browsing goals set by the user in the client (such as a browser) and their actual browsing behavior within a specified time period. This report helps users understand whether their browsing activity during the specified time period aligns with their set browsing goals and provides detailed statistics and recommendations to optimize future browsing behavior.

[0058] Matching Score: This is a quantitative indicator calculated by the system based on a comprehensive analysis of the user's interactive behavior, reflecting the degree of alignment between the user's actions and the set browsing goals. This score can incorporate multiple factors, such as click count, viewing time, likes, comments, and shares. Optionally, the system can also combine the user's interactive behavior and emotional responses to calculate the matching score. Emotional responses refer to sentiment analysis results, such as pleasure level, knowledge acquisition level, and focus level.

[0059] Goal Completion Rate: This refers to the degree to which a user has achieved their set browsing goals, as assessed by the system based on their behavioral data and interaction characteristics. Goal completion rate not only helps users understand their progress towards their goals but also provides feedback to the system, enabling it to optimize recommended content and adjust strategies to ensure users achieve their goals more efficiently. The system can divide completion rate into different stages based on preset thresholds, thus providing more personalized support and guidance.

[0060] Target detection content refers to specific tests or recommended content designed by the system to evaluate and assist users in achieving their browsing goals.

[0061] Historical characteristics information refers to data collected and stored by the system regarding the past behavior and preferences of users.

[0062] Interactive feature information refers to the specific reactions and behavioral data of users to recommended content in the current or recent period.

[0063] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0064] like Figure 1 The diagram shown is an optional application scenario of an embodiment of this application. The application scenario diagram includes a terminal device 110 and a server 120.

[0065] In this embodiment, the terminal device 110 includes, but is not limited to, mobile phones, tablets, laptops, desktop computers, e-book readers, smart voice interaction devices, smart home appliances, and in-vehicle terminals. The terminal device may have a content recommendation-related client installed. This client can be software (e.g., news applications, social media applications, video streaming applications), or it can be a webpage, a mini-program, etc. The server 120 is the backend server corresponding to the software, webpage, or mini-program, or a server specifically used for content recommendation; this application does not impose specific limitations. The server 120 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0066] Specifically, this application proposes a "Goal-Oriented and Intelligent Time Feedback System (hereinafter referred to as the System)," comprising the aforementioned content recommendation-related client (which can also be understood as terminal device 110) and server 120, aiming to solve the problem of users being unable to effectively manage and optimize their time usage when browsing information streams. This system allows users to set specific or vague goals (such as learning new knowledge, finding inspiration, or relaxing) before starting to browse, closely integrating goal management with intelligent technology, and optimizing the browsing experience by monitoring users' behavior and emotional reactions in real time. This innovative method not only helps users clarify their browsing purpose but also effectively improves time utilization efficiency and memory depth through instant feedback and interactive functions, differentiating itself from traditional search engines and recommendation systems. It helps users quickly achieve broad goals, reduces their cognitive burden, avoids the uniformity of conventional recommendations, and significantly improves the overall user experience.

[0067] Optionally, the content recommendation method in each embodiment of this application can be executed by an electronic device, which can be a terminal device 110 or a server 120 in the system. That is, the method can be executed by the terminal device 110 or the server 120 alone, or by the terminal device 110 and the server 120 together.

[0068] For example, when executed jointly by terminal device 110 and server 120, taking a browser as the client, terminal device 110 has a browser installed. When a user wants to perform broad-target browsing in the browser, the browser responds to the user's first setting operation triggered by the current browsing interface and presents a target setting interface. This target setting interface includes at least one preset browsing target. The user can further set browsing targets based on this. Then, in response to the user's second setting operation triggered by the target setting interface, the browser sends a target setting request to server 120 through terminal device 110. After receiving the request, server 120 returns target prompt information to the browser through terminal device 110, thereby causing the browser to present the target prompt information in the target setting interface. This target prompt information is used to guide the user to set the user's browsing target with reference to at least one preset browsing target. After the user sets the user's browsing target, the browser notifies server 120 of the user's browsing target through terminal device 110. Server 120 searches for recommended content related to the user's browsing target and sends the search results to the browser, thereby causing the browser to present a content recommendation interface.

[0069] In one alternative implementation, the terminal device 110 and the server 120 can communicate via a communication network.

[0070] In one alternative implementation, the communication network is a wired network or a wireless network.

[0071] It should be noted that, Figure 1 The examples shown are merely illustrative; in reality, the number of terminal devices and servers is unlimited and is not specifically limited in the embodiments of this application.

[0072] In this embodiment of the application, when there are multiple servers, the multiple servers can form a blockchain, and the servers are nodes on the blockchain; as disclosed in the content recommendation method of this embodiment, the data involved can be stored on the blockchain, such as object browsing targets, recommended content, historical feature information, interaction feature information, etc.

[0073] Furthermore, the embodiments of this application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, and assisted driving. Specifically, it can be mainly applied to browsing information flow content with broad needs in these scenarios, helping users to obtain and utilize information more efficiently. Several examples are briefly listed below:

[0074] For example, if a cloud computing engineer or information technology (IT) professional wants to easily "understand the latest cloud computing trends" and "explore best practices in cloud security," they can set broad learning objectives based on the content recommendation method described in this application. Then, the system will intelligently recommend relevant articles, tutorials, online courses, and case studies to them based on the content recommendation method described in this application, helping them to quickly grasp the latest developments without having to perform specific keyword searches.

[0075] For example, if an artificial intelligence (AI) developer or researcher wants to "find innovative algorithms" or "gain new inspiration for machine learning," they can set these broad goals based on the content recommendation method in this application. Then, the system recommends cutting-edge research papers, open-source projects, code examples, and technical blogs to them based on the content recommendation method in this application, stimulating the user's creative thinking and promoting the progress of the project.

[0076] For example, if a driver or vehicle manufacturer wants to "obtain the latest advancements in autonomous driving technology" or "understand methods to improve driving safety," they can set broad goals based on the content recommendation method in this application. Then, the system recommends relevant information to them based on these broad goals and the content recommendation method in this application, including the latest regulatory updates, technological breakthroughs, and object evaluations, to ensure that the object is always at the forefront of the industry and can make informed decisions.

[0077] It should be noted that the scenarios listed above are merely simple examples. Other scenarios are equally applicable to the embodiments of this application, and will not be elaborated upon here. Through these specific application scenarios, the embodiments of this application not only improve user experience but also promote knowledge dissemination and technological innovation in various fields. Users can easily access high-quality, diverse information resources to meet their personalized and dynamically changing information needs across a wide range of requirements.

[0078] It should be emphasized that the specific embodiments of this application involve relevant data of the object, such as the object's behavioral data (including historical behavior and real-time interactive behavior), the object's behavioral preferences, the object's emotional reactions, browsing history, etc. When the above embodiments of this application are applied to specific products or technologies, permission or consent from the object is required, and the collection, use, and processing of relevant data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0079] The following describes the content recommendation method provided by exemplary embodiments of this application in conjunction with the application scenarios described above and with reference to the accompanying drawings. It should be noted that the above application scenarios are only shown to facilitate understanding of the spirit and principles of this application, and the embodiments of this application are not limited in any way in this respect.

[0080] See Figure 2 The diagram shown is a flowchart of an implementation of a content recommendation method provided in this application. Taking the client as the execution subject as an example, the specific implementation process of this method is as follows: S21 to S23:

[0081] S21: In response to a first setting operation triggered by the current browsing interface, a target setting interface is presented, the target setting interface including: at least one preset browsing target.

[0082] The currently viewed interface refers to the interface on which the user (hereinafter referred to as the user) is currently viewing or interacting with content within the logged-in client (such as an information feed platform). This interface typically displays information feeds that the user is interested in, including but not limited to text, images, and videos. It can also provide various interactive functions, such as scrolling, clicking links, liking, and commenting. It can dynamically update based on the user's actions (such as refreshing), displaying new content or adjusting the arrangement of existing content.

[0083] For example, after an object opens a social media application and enters the main interface, this interface can display updates from followed friends or topics of interest. Each update may contain multimedia content such as text, images, videos, and links. The object can like, comment on, or share these updates. In this scenario, the interface through which the object browses updates is the current browsing interface.

[0084] For example, after logging into a news application, a user enters the homepage. The homepage recommends the latest news articles, trending topics, and special reports based on the user's interest tags. These news items are presented in card format, each containing a title, summary, thumbnail, and other information. Clicking on a card allows the user to access a detailed page to read the full content and participate in comments and interactions. In this scenario, the interface through which the user browses the news is the current browsing interface.

[0085] For example, after launching a video streaming application using an object, the user enters the main interface, which displays short video content in full-screen or list format. Users can browse different videos by swiping up and down. Below each video is usually a simple description and interactive buttons. Users can like, comment, share videos, or click on the creator's avatar to view more works. In this scenario, the interface used by the object to browse the video stream is the current browsing interface.

[0086] It should be noted that the above-listed current browsing interfaces are just simple examples. The current browsing interface will also be different in different information flow platform scenarios. Different current browsing interfaces can display different information flow content to users and provide rich interactive functions, enabling users to efficiently obtain and manage information. These will not be elaborated on here.

[0087] To address the problem of users being unable to effectively manage and optimize time usage when browsing information streams, this application provides a novel goal-setting method that allows users to explicitly express broad and non-specific goals. Specifically, the user first triggers a first setting operation on the current browsing interface, invoking the function of setting a broad goal. The client responds to the first setting operation, presenting a goal-setting interface that includes at least one preset browsing goal. In this goal-setting interface, the user can further trigger a second setting operation to set the current browsing goal (i.e., the user's browsing goal).

[0088] The triggering method for the first setting operation is introduced below:

[0089] Triggering method 1: Control triggering.

[0090] In this application embodiment, the type of control is not specifically limited, and includes, but is not limited to: buttons, icons, menu options, sliders, etc. These controls allow users to trigger the target interface through clicks, swipes, long presses, etc., providing flexible and diverse interaction methods to ensure that different user habits and device requirements are met. Several examples are listed below:

[0091] (1) Button Click: Use an object to click a button on the interface (such as "Set Goal" or "Select Interest") to trigger the goal setting interface. For example, there is a prominent "Set Goal" button on the browser's new tab or homepage. Clicking the object will take you to the goal setting interface.

[0092] (2) Menu Options: Use the object to select a specific option from the drop-down menu or sidebar menu to trigger the target setting interface. For example, use the object to click the three-dot menu in the upper right corner of the browser, select "Set Target", and then enter the target setting interface.

[0093] (3) Icon Click: Use an object to click a specific icon (such as a star, plus sign, etc.) on the interface to trigger the target setting interface. For example, use an object to click the star icon on a new tab, select "Set Target", and enter the target setting interface.

[0094] Triggering method two: Voice trigger.

[0095] For example, users can trigger the target setting interface by speaking specific voice commands through a voice assistant, such as, "Hey, [user's name], help me set my browsing target." After recognizing the command, the voice assistant opens the browser and redirects to the target setting interface. Alternatively, users can engage in multiple rounds of dialogue with the voice assistant, gradually guiding them to the target setting interface.

[0096] Triggering method 3: Gesture triggering.

[0097] In this embodiment, the type of gesture that triggers the first setting operation is not specifically limited, and includes, but is not limited to, swiping, long pressing, pinching, etc. These gestures allow the user to interact with the client through intuitive actions (such as swiping up to open the target setting interface), providing a flexible and convenient operation method, adapting to different devices and user habits, ensuring broad applicability and optimized user experience. Several examples are listed below:

[0098] (1) Swipe gesture: Use an object to trigger the target setting interface by using a specific gesture (such as swiping up or pinching two fingers) on the screen. For example, swiping up on the browser homepage with an object will bring up a shortcut menu that includes the option to "Set Target".

[0099] (2) Long press trigger: Use an object to long press an element (such as the homepage icon or bookmarks bar) to trigger the target setting interface. For example, use an object to long press the browser homepage icon, a menu will pop up, and select "Set Target".

[0100] Triggering method four: Shortcut key trigger.

[0101] For example, an object can trigger a target settings interface by pressing a specific keyboard combination.

[0102] Triggering method five: Automatic triggering.

[0103] In this embodiment, the conditions for automatically triggering the first set operation are not specifically limited, but include, but are not limited to: prolonged user stay, application launch, opening of a new tab, specific time period or location, browsing behavior patterns, etc. The client or server intelligently judges and prompts the user to set a target based on these conditions, improving user experience and ease of operation. Several examples are briefly listed below:

[0104] For example, the browser can automatically prompt whether a target needs to be set based on the user's current behavior or browsing history. For instance, if the user remains on a blank page for an extended period, the browser can automatically display a prompt asking, "Do you want to set a browsing target?" and providing an option to access the target setting interface.

[0105] For example, users can be regularly reminded to set or adjust their browsing goals, especially when they launch their browser or open a new tab. For instance, when a user opens their browser for the first time each morning, the system might prompt, "What are your browsing goals for today?", allowing them to access the goal setting interface.

[0106] It should be noted that the triggering methods for the first setting operation listed above are merely simple examples. Other triggering methods are also applicable to the embodiments of this application, and will not be elaborated upon here. Through these diverse first setting operation triggering methods, users can easily access the target setting interface according to their habits and needs, setting or adjusting their browsing targets. This not only improves the user experience but also makes personalized content recommendations more accurate and efficient.

[0107] Next, we will explain the relationship between the target setting interface and the currently viewed interface:

[0108] After the first setting operation is triggered using the object, the client displays the target setting interface. Optionally, the target setting interface can be an overlay interface superimposed on the current browsing interface, such as a floating layer, pop-up window, sidebar, bottom drawer, top notification bar, etc. The target setting interface can also be an independent interface separate from the current browsing interface, such as a new tab or new window.

[0109] Specifically, overlay interfaces are directly superimposed on the current browsing interface, allowing users to set targets without completely leaving the current page. This approach maintains context awareness for the user and provides a fast switching and convenient operation experience.

[0110] In the embodiments of this application, a floating layer refers to a layer that is overlaid on the current browsing interface.

[0111] Optionally, the height of the overlay is adjustable, and it can be a full-screen overlay, a half-overlay (50% of the screen height and the same width as the screen), or other custom sizes; the transparency of the overlay is also adjustable, and it can be semi-transparent (such as 30%-70% transparency), opaque (100% opaque), or other custom transparency.

[0112] This overlay design ensures that users can set their targets without completely obscuring the underlying content, providing a flexible and professional visual experience. For example, when a user clicks the "Set Target" button on the current webpage, an opaque overlay pops up, covering the current webpage and displaying the preset browsing target options.

[0113] In this embodiment, a pop-up window refers to a layer that occupies a portion of the screen and obscures the current browsing interface but does not completely cover it.

[0114] This pop-up design effectively captures the user's full attention, ensuring they focus on setting their goals. Users can easily exit the pop-up and return to the current browsing interface using a close button. For example, after clicking the "Set Goal" button on the current webpage, a centered pop-up appears, displaying preset browsing goal options.

[0115] In this embodiment, the sidebar slides in from one side of the screen, the bottom drawer slides up from the bottom of the screen to occupy the lower half of the screen, and the top notification bar appears at the top of the screen in the form of a small notification bar.

[0116] All of these designs allow users to set goals while browsing, making them suitable for operations that require frequent switching; and they don't take up too much screen space, making them particularly suitable for mobile devices. For example, after a user clicks the "Set Goal" button on the current webpage, a sidebar slides in from the left, providing preset browsing goal options, which the user can select and apply immediately; another example is that after a user clicks the "Set Goal" button on the current webpage, a drawer-style panel slides out from the bottom, providing preset browsing goal options; and so on.

[0117] Standalone interfaces, on the other hand, are completely independent of the current browsing interface, typically opening in a new tab, window, or full-screen mode. This approach provides a clearly separated environment, suitable for operations requiring detailed settings or complex interactions.

[0118] It should be noted that the above-listed goal-setting interface designs are merely simple examples. Other forms of goal-setting interfaces are also applicable to the embodiments of this application, and the specific choice depends on the user experience design goals and platform characteristics. By rationally designing the relationship between the goal-setting interface and the currently viewed interface, the user's operational efficiency and satisfaction can be improved, making the goal-setting process more natural and smooth. These will not be elaborated upon further here.

[0119] Next, we will introduce the preset browsing targets in the target setting interface:

[0120] The target setting interface in this application embodiment includes at least one preset browsing target for users to select and refer to.

[0121] Specifically, preset browsing goals refer to a series of general and common browsing goals that are predefined by the system (such as an information flow platform) to help users achieve their broad browsing objectives more efficiently.

[0122] In this application embodiment, these preset browsing targets can be designed based on extensive user behavior data and typical usage scenario analysis. Several preset browsing targets are briefly listed below:

[0123] (1) Learn new knowledge. The pre-set browsing goal of "learn new knowledge" indicates that the user wants to acquire new knowledge or skills through browsing, but there is no clear learning path or specific topic.

[0124] When a user sets a specific browsing goal as their browsing objective, the system can provide them with relevant knowledge content that falls outside their cognitive boundaries, based on their learning objectives, areas of interest, and existing knowledge base. This content can include educational resources such as online courses, tutorials, and technical blogs. Additionally, it can provide learning progress tracking and the latest news in related fields. This new knowledge is presented in the form of specific knowledge points and hashtags, ensuring it doesn't exceed the user's comprehension while effectively stimulating their learning interest and motivation, thus guaranteeing that the content meets user needs and expands their cognitive boundaries.

[0125] This approach not only helps users easily find suitable learning resources, but also improves learning efficiency and experience through personalized recommendations.

[0126] (2) Finding inspiration. The pre-set browsing goal of "finding inspiration" means that the user hopes to stimulate creativity or find new ideas to solve problems through browsing, which usually involves extensive information exploration.

[0127] When the user sets this browsing target as their browsing goal, the browser (referring to the client) can provide creative resources and inspiring content based on the user's functional needs, using the browser's (client's) movie, novel, game, and file management (images, videos, cloud storage, documents, notes, etc.) functions. For example, it can recommend innovative product cases, designer interviews, creative works displays, and other content that provides cross-disciplinary inspiration, such as art, technology, and business, to help the user gain inspiration for creation or thinking.

[0128] (3) Entertainment and relaxation. The pre-set browsing goal of "entertainment and relaxation" indicates that the users hope to have a relaxed and enjoyable experience during the browsing process, relieve stress or pass the time.

[0129] When a user sets a specific browsing target as their browsing objective, the browser can provide a variety of entertainment content based on the user's leisure needs and interests. This can be achieved through the browser's features such as movies, novels, games, news, and short dramas. Examples include recommending funny videos, relaxing music playlists, humorous articles or comics, and providing interactive entertainment such as mini-games and fun quizzes. This content helps users relieve stress, improve their mood, and enjoy their leisure time in a relaxed and pleasant atmosphere.

[0130] (4) Leisure reading. The presupposed browsing goal of "leisure reading" indicates that the users hope to enjoy a relaxed reading experience through browsing, covering various types such as news, stories, and commentary.

[0131] When a user sets a specific browsing target as their browsing objective, the browser's news, novels, and information features can provide a wide variety of reading materials based on the user's interests and reading habits. For example, it can recommend trending news, short stories, reader reviews, and excerpts from classic literary works.

[0132] In this embodiment, the system's predefined browsing goals provide a series of common and useful options to help users quickly express their browsing intentions, and an intelligent content recommendation mechanism ensures that users can efficiently achieve their goals. This design not only enhances the user experience but also promotes the effective transmission and utilization of information.

[0133] It should be noted that the preset browsing targets listed above are merely simple examples, and other preset browsing targets are also applicable to the embodiments of this application, and will not be described in detail here. Furthermore, although the preset browsing targets are predefined by the system, this application supports users in adjusting or selecting multiple target combinations according to their specific needs.

[0134] In the target setting interface, the preset presentation methods for browsing targets can be diversified to ensure that users can intuitively and quickly select and understand these targets. The following are some common presentation methods:

[0135] Presentation Method 1: Circular Layout. In this method, the preset browsing targets are arranged in a circular or circular pattern, with each target occupying one sector.

[0136] For example, after clicking "Set Target" on the user, a circular menu appears. Each sector represents a preset browsing target. The sector can contain a target icon, a short text description, etc., such as "Learn New Knowledge", "Find Inspiration", "Entertainment and Relaxation". Users can select preset browsing targets of interest by sliding or other means, or they can customize browsing targets by referring to these preset browsing targets.

[0137] Presentation Method 2: Card Layout. In this method, the preset browsing targets are displayed in the form of cards, each containing the target name, a brief description, and a related icon.

[0138] For example, after clicking "Set Target" on the user interface, a card-style interface will appear, where each card displays a preset browsing target. The user can select the card of interest to confirm, or they can refer to these cards to customize their own browsing target.

[0139] Presentation Method 3: List Layout. In this method, the preset browsing targets are displayed in a list format, with each target on a separate line, typically including a name and a brief description.

[0140] For example, after clicking "Set Target" on the object, a list interface will appear, showing multiple preset browsing targets. The object can select a preset browsing target of interest for confirmation, or it can refer to this list to customize its own browsing target.

[0141] Presentation Method 4: Icon-based Layout. In this method, preset browsing targets are displayed as icons, with each icon representing a target category and accompanied by a brief text description.

[0142] For example, after clicking "Set Target" on the user interface, an icon interface will appear. Each icon represents a preset browsing target. Users can select the icon of interest to confirm, or they can refer to these icons to customize their browsing target.

[0143] Presentation Method 5: Carousel Layout. In this method, preset browsing targets are displayed in a carousel format, and users can swipe left or right to view different targets.

[0144] For example, after clicking "Set Target" on the user interface, a carousel interface will appear, displaying multiple preset browsing targets. The user can select the target of interest by swiping left or right.

[0145] It should be noted that the above-listed methods for presenting preset browsing targets are merely simple examples. Other presentation methods are also applicable to the embodiments of this application. Furthermore, combinations of multiple presentation methods are possible, which will not be elaborated upon here. In practical applications, the presentation method of preset browsing targets can be selected based on specific application scenarios and user experience requirements. Whether it's the visual appeal of a circular layout, the information richness of a card-style layout, or the simplicity of a list-style layout, each method has its unique advantages. Through reasonable selection and design, users can more easily set and select their browsing targets, improving the overall user experience.

[0146] Taking a browser as an example, when the browser is opened using an object, the target settings interface can be accessed through any of the methods listed above.

[0147] See Figure 3 As shown, this is a schematic diagram of a current browsing interface and a target setting interface in an embodiment of this application. When the user opens the browser, they enter the browser homepage. Figure 3In the middle, the left-hand interface is the current browsing interface, i.e., the browser homepage. Within this interface is a "Set Target" button, as shown in dashed box 31. Clicking this button will display a simple, clear, and intuitive target setting interface as a floating layer on the current browsing interface, as shown below. Figure 3 As shown in the dashed box 32 on the right, the target setting interface displays preset browsing targets, such as "learn new knowledge", "find inspiration", "entertainment and relaxation", etc., to guide users to clarify the main purpose of this browsing. Users can set their browsing targets by selecting preset browsing targets or by custom input.

[0148] In this embodiment of the application, when an object sets the browsing target in the target setting interface, the specific process is as follows:

[0149] S22: In response to a second setting operation triggered through the target setting interface, a target prompt message is displayed in the target setting interface; the target prompt message is used to guide the user to browse the target by referring to at least one preset target setting.

[0150] Similar to the first setting operation described above, the second setting operation can also be triggered in many ways, including but not limited to: control triggering, voice triggering, gesture triggering, shortcut key triggering, keyboard (which can be a virtual keyboard) triggering, etc. Of course, it can also be a combination of one or more methods, such as a combination of control triggering and voice triggering, etc., which will not be elaborated on here.

[0151] As mentioned above Figure 3 As shown in the example, in the target setting interface shown in the dashed box 32, the preset browsing targets adopt a circular layout, such as "Learn new knowledge", "Find inspiration" and "Entertain and relax". Each preset browsing target occupies a sector. Users can select different targets by swiping gestures or by clicking on a specific sector to select directly.

[0152] In addition, a central button is set in the center of the ring, such as "Press and speak" in the dashed box 321. The user is prompted to press and hold the button to say the desired browsing target. After pressing and holding the button, the user can say the selected preset browsing target or the custom browsing target by voice.

[0153] Of course, in addition to the "Press and hold to speak" button shown in 321, the center button can also be a keyboard icon. Users can use this keyboard icon to bring up the virtual keyboard and input their browsing target by typing or other means.

[0154] Alternatively, users can choose to switch between different input methods, such as voice input, keyboard input, or even other input methods, which will not be listed here.

[0155] It should be noted that the triggering methods of the second setting operation listed above are just simple examples. Other triggering methods are also applicable to the embodiments of this application, and will not be described in detail here.

[0156] In this embodiment of the application, the target prompt information includes a series of auxiliary information presented by the client after the user triggers the second setting operation through the target setting interface, which is used to help the user understand the preset browsing target more clearly and guide him to set the object browsing target for this time (hereinafter referred to as the target).

[0157] Optionally, in the embodiments of this application, the system can, during the process of setting the browsing target by the user, determine and analyze in real time whether the target currently entered by the user is a clear target or a vague target based on the content entered by the user (for details, please refer to the relevant description on the server side, which will not be repeated here), and based on the determination result, push relevant and different target prompt information to the user in real time, such as the first target prompt information, the second target prompt information, etc. in this application.

[0158] In one optional implementation, S22 includes, but is not limited to, at least one of the following operations ( Figure 2 (Not shown):

[0159] S221: Display the first target prompt information in the target setting interface.

[0160] In this embodiment of the application, the first target prompt information includes: suggestions for setting browsing targets, which are used to comprehensively help users set browsing targets that meet their needs more efficiently and accurately.

[0161] Optionally, the browsing target settings may include, but are not limited to, one or more of the following:

[0162] (1) Setting rule reminders. Specifically, setting rule reminders provides basic rules and precautions on how to set effective browsing goals. For example, "Goals should conform to the smart rules as much as possible" and "Please try to choose a clear direction so that the system can better recommend content," etc., to guide users to set reasonable and achievable goals, avoid setting unrealistic or difficult-to-achieve goals, and improve user experience and satisfaction.

[0163] Smart rules are a method for setting clear and achievable goals, ensuring that the goals are specific, measurable, achievable, relevant, and time-bound. For example, "Learn the badminton serve in 1 hour and understand the key points" specifically refers to what to learn, is measurable by mastering the key points, achievable within 1 hour, relevant to the user's interests, and time-bound (1 hour). This approach helps users set clear and feasible goals. For instance, the smart rule in this article could be specifically set as: Do xx thing for xx time to achieve xx goal. Of course, other smart rules can also be used, which will not be elaborated here.

[0164] like Figure 4 As shown, this is a schematic diagram of a first target prompt message in an embodiment of this application. For example, when a user presses and holds the "Press and hold to speak" button, they say their target, "Learn badminton serve for 1 hour." At this point, the target entered by the user is a basically clear target, and the user can be prompted that "the target should conform to the smart rules as much as possible." Figure 4 As shown in the dashed box 41, we should try to guide users to follow the SMART principle when setting browsing goals, so as to clarify the goals.

[0165] Furthermore, when selecting a preset browsing target for an object, subtle animation effects, such as sector zooming or color changes, can be added to the corresponding preset browsing target to enhance interactivity. Similarly, when defining a custom browsing target for an object, it can be assigned to a specific preset browsing target range, thereby adding subtle animation effects to the corresponding preset browsing target, and so on.

[0166] When the system detects that the input from the user is related to learning new knowledge, it will categorize it under the broader category of "learning new knowledge." For example... Figure 4 As shown in the dashed box 42, "Learn badminton serve for 1 hour" belongs to "Learn new knowledge". Therefore, the sector boundary of the "Learn new knowledge" sector can be thickened. Of course, other animation effects can also be used, which will not be elaborated here.

[0167] (2) Set directional prompts. Specifically, directional prompts can be short texts or icons to guide users to think about and clarify their browsing intentions. For example, "You can set a time and measurable results for yourself," "You can set the skills you hope to gain through browsing," "It is recommended to clarify the knowledge areas you want to learn about," "Specifically describe the areas where you are looking for inspiration," "You can specify the length of this entertainment and relaxation session," etc., to help users think more deeply and ensure that the set goals are more specific and personalized, avoiding overly broad or vague goal settings.

[0168] like Figure 5 As shown, this is a schematic diagram of another first target prompt message in an embodiment of this application. For example, the user presses and holds the "Press and hold to speak" button and says the user's browsing target, namely "to relax and have some fun." At this time, the user's current input target is vague, and it is not yet clear how long the user wants to relax or to what extent. In this case, the user can be prompted "You can give yourself a time and a measurable result," such as... Figure 5 As shown in the dashed box 51, efforts should be made to guide users to clearly define their goals.

[0169] When the system detects that the user's input is related to entertainment, it will categorize it under the broader category of "Entertainment and Relaxation." For example... Figure 5 As shown in the dashed box 52, "Relax and have some fun" belongs to "Relax and have fun". Therefore, the sector boundary of the "Relax and have fun" sector can be thickened. Of course, other animation effects can also be used, which will not be elaborated here.

[0170] Optionally, after the user presses and holds the "Press and Hold to Talk" button, the button will change to "Release to Send," prompting the user to release the button to complete the browsing target settings.

[0171] (3) Examples of desired browsing goals. Specifically, these examples can provide specific preset browsing goal examples, such as "learn badminton serve for one hour", "watch funny videos for half an hour", "read innovative product case studies for 30 minutes", etc., to provide users with intuitive selection references, simplify the setting process, and help them quickly find goals that meet their needs.

[0172] (4) Sharing success stories. Specifically, showcase success stories or user experiences of other users who set browsing goals, such as "Xiaoming chose 'Learn new knowledge' and mastered the basics of Python within a month." In this way, enhance the user's confidence through real-world examples, stimulate their interest in setting similar goals, and provide specific practical references.

[0173] It should be noted that during the process of an object triggering the second setting operation to set a browsing target, the first target prompt information can also change dynamically. Specifically, when an object triggers the second setting operation and begins setting a browsing target, the first target prompt information can be dynamically adjusted based on the object's interaction, thereby providing the object with more personalized suggestions.

[0174] For example, if the user hasn't set any browsing goals, remind them to "learn badminton serve for one hour." If the user has set an initial browsing goal, such as "relax and have some fun," but hasn't confirmed the final browsing goal, remind them to "set a timeframe and a measurable result." Similarly, if the user hasn't set any browsing goals, remind them that "Xiaoming chose 'Learn new knowledge' and mastered Python basics within a month." If the user has set an initial browsing goal, such as "Learn programming," but hasn't confirmed the final browsing goal, remind them to "specify the length of time spent learning programming."

[0175] Furthermore, it should be noted that the first target prompt information in this application can also be presented to the user before the user triggers the second setting operation, providing the user with a preliminary reference. As mentioned above... Figure 3 For example, in the target setting interface shown in the dashed box 32, in addition to presenting three preset browsing targets in a circular layout, a central button is set in the center of the ring, and a prompt is given above the ring: "Long press to output your small goal by voice" "e.g.: Learn badminton serving for 1 hour and understand the key points of serving".

[0176] The first target prompt information listed in S221 above is one type of target prompt information in this application. In addition, the second target prompt information in S222 is another type of target prompt information in this application. These two types of target prompt information can exist simultaneously, or only one type can exist, or they can appear one after the other, etc. This article does not make specific limitations.

[0177] S222: In the target setting interface, a second target prompt message is presented in the form of a dialogue; wherein, the second target prompt message includes: a guiding question generated based on the user's current initial browsing target; the guiding question is used to guide the user to update the initial browsing target to the user's browsing target, further refining and optimizing the user's browsing target setting.

[0178] In this embodiment, personalized guiding questions are generated based on the user's current initial browsing goals, ensuring the relevance and relevance of the questions; furthermore, the use of a dialogue format can further enhance the user's sense of participation and interactive experience.

[0179] For example, if the user selects "Learn new knowledge" as the initial browsing goal, the current target entered by the user is vague. The user may be presented with guiding questions in the form of a dialogue: "Which area of ​​knowledge do you want to learn?", "What form of learning do you hope to use?", "Do you have specific learning goals or time arrangements?", "What skills do you want to master?", etc.

[0180] For example, if the user selects "finding inspiration" as the initial browsing goal, the current target entered by the user is also a vague target. The following dialog-style guiding questions may appear: "Could you describe in more detail the area where you are looking for inspiration?", "How long would you like to browse?", etc.

[0181] For example, if the user selects "entertainment and relaxation" as the initial browsing goal, the current goal entered by the user is also a vague goal. The following dialog-style guiding questions may be presented: "What kind of entertainment do you like?", "How long do you want to relax?", etc.

[0182] During the dialogue, users can input answers based on system questions to refine their goals.

[0183] Optionally, to reduce the input burden on users, the second target prompt information may also include: preset answer options corresponding to the guiding question. In this way, users only need to select the answer that best suits their needs from multiple options, without having to perform complex text input, which further simplifies the operation steps for users.

[0184] For example, the guiding question is "What field of knowledge would you like to learn?", with preset answer options including: programming, art, history, any, custom; the guiding question is "What form of learning would you like to use?", with preset answer options including: online courses, articles, video tutorials, any, custom; the guiding question is "How long would you like to relax and have fun?", with preset answer options including: 10 minutes, 30 minutes, one hour, two hours, any, custom; the guiding question is "What kind of entertainment do you like?", with preset answer options including: watching funny videos, playing mini-games, browsing humorous comics, participating in fun quizzes, any, custom; and so on.

[0185] like Figure 6 As shown, this is a schematic diagram of a second target prompt message in an embodiment of this application. For example, in Figure 5 Building upon this foundation, in addition to suggesting to users that they "can add a timeframe and measurable outcome," further guidance can be provided through dialogue to help users clarify their goals, such as... Figure 6 As shown in the dashed box 61, the system asks the user "How much time do you want to relax and have fun?" and provides several preset answer options, such as: 10 minutes, 30 minutes, one hour, two hours, or whatever.

[0186] Users can choose a preset answer option. For example, if a user selects "2 hours," their browsing goal will change from "relax and have fun" to "relax and have fun for 2 hours." Conversely, if a user selects "whatever," it means they haven't specified a time limit for relaxation, and their browsing goal remains "relax and have fun." Users can also input their own answers, such as "40 minutes," which will update their browsing goal from "relax and have fun" to "relax and have fun for 40 minutes." This article does not impose specific restrictions on this.

[0187] In the above embodiments, presenting the first target prompt information helps users quickly understand and select a preset browsing target that meets their needs. This approach simplifies the user's setup process and improves the user experience by providing an intuitive selection reference. Presenting the second target prompt information in a dialog format helps users think more deeply about their browsing intentions, thereby setting more specific and actionable browsing targets. This not only improves the user experience but also enhances the effectiveness and practicality of target setting.

[0188] Of course, the goal-setting information in this application is not limited to expected browsing goal examples and guiding questions, but may also include other forms, such as interactive elements, including buttons, sliders, ratings, etc., such as "Select the range of topics you are interested in" (slide selection) and "Rate your current knowledge level" (star rating). By presenting interactive elements in the goal-setting interface, the user's sense of participation can be increased, making the goal-setting process more dynamic and interesting.

[0189] In addition, charts, pictures, videos and other formats can be used to display the effects or application scenarios of different preset browsing targets, thereby attracting the attention of the target audience through visual effects and helping them to better understand the actual meaning of each target.

[0190] In summary, the target prompt information in the embodiments of this application is not only for providing suggestions or guidance, but also includes a variety of auxiliary forms. By combining these different forms of target prompt information, the system can more effectively guide users, comprehensively help users set and achieve their browsing goals, and ensure that the goals they set are both in line with their personal needs and are operable, thereby improving the overall user experience and satisfaction.

[0191] In this embodiment, there can be multiple guiding questions, each with preset answer options, to help the user more specifically set their browsing goals. Therefore, an optional implementation of S222 is as follows:

[0192] In the goal setting interface, the second goal prompts are presented in the form of multiple rounds of dialogue. Each round of dialogue corresponds to a guiding question, and the second goal prompts also include preset answer options for the guiding question.

[0193] The order of the multiple boot questions can be pre-set by the system.

[0194] For example, after a user selects "Learn New Knowledge," the system will sequentially ask for information on the knowledge area, learning format, goals, and time schedule, and provide preset options such as "Programming," "Online Courses," and "Two Hours Per Week," simplifying the user's operation and ensuring that goals are set accurately and achievably. This approach improves user experience and the efficiency of goal setting.

[0195] Furthermore, similar to the first target prompt information mentioned above, the second target prompt information in this application can also change dynamically. In multi-turn dialogues, the system is allowed to evaluate and adjust the content of subsequent questions in real time based on the user's answers to each guiding question, ensuring that the final target is more in line with the user's actual needs.

[0196] For example, if a user selects "Learn New Knowledge" as their initial browsing goal, the system might first ask, "Which area of ​​knowledge would you like to learn about?" If the user selects "Programming," the system will further refine the question: "In what form would you like to learn programming?", providing preset options such as "Online Courses" or "Article Reading." Based on the user's further selections, the system can continue to ask more specific questions, such as "Do you have specific learning goals or a time schedule?"

[0197] For example, if a user selects "finding inspiration" as their initial browsing goal, the system might first ask, "What area of ​​inspiration do you hope to gain?" If the user selects "product design," the system then asks, "Would you like to view other designers' portfolios for inspiration?" If the user selects "yes," the system can recommend portfolios of well-known designers and articles on the latest design trends. In this way, the user's browsing goal changes from the broad "finding inspiration" to the specific "referencing the work of well-known designers to inspire product design," greatly improving the operability and practicality of the goal.

[0198] like Figure 7The diagram shown illustrates a multi-turn dialogue in an embodiment of this application. Assuming the user selects "Learn New Knowledge" as their initial browsing goal, the first round of dialogue can ask the user "Which field of knowledge do you want to learn?" and provide preset answer options: programming, art, history, ... (Due to screen size limitations, the user can swipe left and right to view more preset answer options), or custom. The user can choose a specific preset answer option or select "custom" to input their own answer.

[0199] For example, if the user selects "programming" and the browsing goal is updated to "learning new programming knowledge", then in the second round of dialogue, you can ask the user "How long do you want to study?". The preset answer options are: 10 minutes, 30 minutes, 1 hour, ..., custom. Similarly, the user can choose a specific preset answer option, or choose "custom" to enter their own answer.

[0200] Optionally, for any guided question, if the user does not answer within a timeout period (e.g., more than 2 minutes), the user can be asked again in a different way, or the user can be assumed to have selected "anything".

[0201] The above implementation further refines the presentation of the second target prompt information, emphasizing a multi-turn dialogue format and introducing preset answer options for guiding questions. Each round of dialogue corresponds to a specific guiding question, and users can choose preset answer options to simplify the operation. This approach not only provides more detailed and personalized guidance but also reduces the user's input burden and improves setting efficiency through preset answer options. Furthermore, the multi-turn dialogue format allows the system to dynamically adjust subsequent questions based on the user's answers. Each round of dialogue is adjusted based on the previous round's answers, making the questions more targeted and personalized, avoiding unnecessary information redundancy. Moreover, through gradual guidance via multi-turn dialogue, the system can collect more specific information about the user's needs, thereby generating more accurate browsing goals and ensuring that the final set goals are more aligned with the user's actual needs and are more practical. This approach not only improves user satisfaction but also ensures an efficient and enjoyable browsing experience. This highly interactive and flexible setting method significantly enhances user experience and the accuracy of goal setting.

[0202] It should be noted that in practical applications, the guidance issue only guides the user to further refine the browsing target. However, the user may not necessarily refine it during this process. In that case, the browsing target may not change before and after the update of "updating the initial browsing target to the object browsing target," meaning the object browsing target is the initial browsing target set by the user before guidance. Of course, if the user refines the target, then the object browsing target in this application is obtained by refining the initial browsing target through guidance.

[0203] S23: After setting the object browsing target using the object, a content recommendation interface is presented, which includes recommended content related to the object browsing target.

[0204] In this embodiment of the application, after the user sets the browsing target, the system can combine one or more factors such as the user's historical behavior, current time, popular trends, and the user's instant feedback (such as instant interactive behavior, emotional reaction, etc.) and use one or more technologies such as advanced Natural Language Processing (NLP), sentiment analysis, and multimodal content analysis to search for suitable recommended content, thereby providing the user with diversified, personalized and inspiring content recommendations.

[0205] For example, for vague targets, the system supports further refining the targets based on the user's real-time feedback (such as dwell time, click behavior, etc.) to improve the accuracy of search results, thereby gradually improving the accuracy of recommendations, achieving a better user experience and higher user retention rate.

[0206] It should be noted that the above is only a brief overview and example of how to search for recommended content. The specific search process will be explained on the server side later, and will not be repeated here.

[0207] In one alternative implementation, after setting the browsing target using an object, the client immediately presents a portion of recommended content related to that target, and subsequent searches can continue. The recommended content is updated using a refresh operation on the object.

[0208] In another alternative implementation, after setting a goal using an object, the client can first provide some search suggestions, and then present a portion of recommended content related to that goal. The client can continue searching and update the recommended content using the object through a refresh operation.

[0209] In this application embodiment, an optional implementation of S23 is as follows, including S231 to S232 ( Figure 2 (Not shown):

[0210] S231: After using the object setting to browse the target, at least one content preview card is displayed in the target setting interface, and at least one content preview card is dynamically updated during the recommended content search process. At least one content preview card is generated based on the currently searched recommended content, and each content preview card corresponds to one recommended content.

[0211] In this embodiment, the content preview card is automatically generated based on several recommended contents found in the current search. It is a kind of search reminder and is mainly used to display the visual information of the content, such as the content cover image, so as to intuitively convey the core theme and style of the content and attract the attention of the target audience.

[0212] In other words, content preview cards can provide users with immediate visual feedback, helping them quickly assess the relevance of recommended content while avoiding premature distraction. Therefore, these content preview cards generally do not have the function of jumping to content details, meaning users cannot directly interact further or obtain detailed information through the content preview cards.

[0213] Of course, these content preview cards can also be designed to jump to the content details. In this way, in the target setting interface, users can directly access the relevant content details by clicking on a content preview card, thereby gaining a deeper understanding of the content of interest while setting goals, improving browsing efficiency and user experience.

[0214] The following example primarily uses a content preview card, which refers to a content cover image that does not have the function of jumping to the content details:

[0215] After users set browsing goals, at least one content preview card is immediately displayed in the goal setting interface. These cards are generated based on the initially matched recommended content and are used to provide users with immediate feedback and initial selection. This ensures that users can see relevant recommended content immediately while setting goals, enhancing the consistency and immediacy of the user experience.

[0216] Furthermore, as the search for recommended content continues, the content preview cards are updated in real time. For example, each time new and more relevant content is found, the content preview cards are dynamically refreshed to ensure that users always see the latest and most relevant recommendations.

[0217] Specifically, it can be divided into the following stages: initial presentation and dynamic refresh:

[0218] (1) Initial presentation stage: After setting the object browsing target using the object, the system will generate and display the first batch of content preview cards through the client based on the initial matching results.

[0219] (2) Dynamic refresh phase: As the search process progresses, the system continuously discovers more relevant content and dynamically replaces the existing content preview cards to ensure that the card content is always up-to-date and most relevant.

[0220] Optionally, during the dynamic refresh phase, the number of content preview cards is fixed, while the card content is continuously updated.

[0221] Assuming there are 3 cards, such as Figure 8 As shown, it is a schematic diagram of a content preview card in an embodiment of this application. Wherein, Figure 8 The interface shown on the left, 81, indicates that the user long-pressed "Press and hold to speak" and directly entered a clear goal, "Learn badminton serve for 1 hour." The system recognized the user's goal intent and displayed a goal prompt message on the client side: "The goal should conform to the SMART rules as much as possible." Figure 8 As shown on the left side of the interface; then, Figure 8 The interface 82 shown on the right indicates that when the object releases this button, the system will intelligently match and recommend relevant content based on the target content (i.e., "learn badminton serve for 1 hour"), and present the content preview card as shown in the dashed box 821 in the target setting interface. Three content cover images are displayed in the dashed box 821, and three new content cover images will be displayed in subsequent updates.

[0222] For example Figure 9 As shown, this is a schematic diagram of another content preview card in an embodiment of this application. Wherein, Figure 9 Interface 91 on the left shows that when a user long-presses "Press and hold to speak" and directly inputs a vague goal, "Learn badminton serve for 1 hour," the system can intelligently guide the user step-by-step to input a more specific goal based on the goal's content, the user's behavioral preferences, and interest tags. This includes selecting a corresponding time period (as shown in interface 92) and a specific category. As shown in interface 92, when the user selects "Anything," meaning they still don't choose a specific goal, the system uses semantic analysis and the user's behavioral preferences and interest tags, leveraging big data analysis and machine learning algorithms to filter relevant and high-value content from a vast amount of online information (for example, the system will prioritize recommending lighthearted and interesting videos, humorous articles, or soothing music). A content preview card is then displayed in the dotted box 931 shown in interface 93, which also displays three content cover images. Three new content cover images will be displayed in subsequent updates.

[0223] Optionally, changes to the card content can be indicated to the recipient through visual effects (such as fade-in / fade-out, swipe, etc.) to make the recipient aware that the recommended content is being updated, which will not be elaborated on here.

[0224] Once the preset search criteria are met, the user will be automatically redirected to the content recommendation interface, as shown in S232 below:

[0225] S232: After the preset search criteria are met, the user will be redirected to the content recommendation interface.

[0226] In this embodiment, when the preset search criteria are met, the client will automatically redirect to the full content recommendation interface, providing a richer browsing experience. Of course, after redirecting to the content recommendation interface, users can continue searching for new recommended content, or refresh the page to search for new recommended content again, and so on.

[0227] Optionally, the relationship between the content recommendation interface and the current browsing interface can be flexibly designed. For example, they can be the same interface; that is, after meeting the preset search criteria, the target setting interface is closed directly, the current browsing interface is refreshed, and the newly searched recommended content is displayed in the current browsing interface. Alternatively, they can be different interfaces; after completing the target setting, the user is redirected to a dedicated content recommendation interface to enjoy more comprehensive recommended content. This approach maintains the continuity of the user experience while providing richer browsing options.

[0228] In this embodiment of the application, the preset search criteria refer to the system's decision on when to jump to the content recommendation interface or stop dynamically updating the content preview card during the recommended content search process, based on specific criteria.

[0229] Optionally, preset search criteria include, but are not limited to, one or more of the following:

[0230] Search criterion 1: A certain amount of content is found. Specifically, when the system finds a certain number of relevant recommended content (e.g., 5-10 items), it considers that enough choices have been provided and can redirect to the content recommendation interface.

[0231] Search criterion two: Search lasts for a certain period of time. Specifically, after a certain period of time (e.g., 10 seconds or 30 seconds), even if not enough content is found, the user will be automatically redirected to the content recommendation page to avoid long waiting times.

[0232] Search criterion three: Achieving a certain degree of matching. Specifically, the system uses an algorithm to evaluate the matching degree of the found content. When the matching degree reaches a certain threshold (e.g., 80%), it is considered that high-quality relevant content has been found, and the user can be redirected to the content recommendation interface.

[0233] Search criterion four: The object triggers a specific action. Specifically, if the object triggers certain actions, such as closing the target settings interface or refreshing the current browsing interface, the system will immediately stop the search and display the currently searched recommended content.

[0234] For example, if a user clicks on a content preview card, indicating interest in the initial recommended content, the system can either redirect to the content details page or to the content recommendation interface to display the currently searched recommended content.

[0235] Search criterion five: Meets the user's behavior patterns. Specifically, based on the user's browsing habits and behavior patterns (such as dwell time, scrolling speed, click frequency, etc.), the system intelligently determines when to stop searching and display recommended content.

[0236] Search criterion six: Resource limitations. Specifically, when system resources reach their limits (such as CPU usage, memory consumption, etc.), the system will prematurely end the search and display the existing results to ensure performance.

[0237] The above implementation significantly improves user experience and system responsiveness by instantly displaying at least one content preview card on the target setting interface and dynamically updating these cards during the recommended content search process. After setting a browsing target, users can immediately see relevant recommended content and receive instant feedback, enhancing the continuity and satisfaction of the operation. Furthermore, the dynamic update mechanism ensures that the card content is always up-to-date and highly relevant, avoiding the waste of time waiting for complete search results.

[0238] Furthermore, once the preset search criteria are met, the system automatically redirects to a content recommendation interface, providing a more comprehensive range of browsing options. This approach not only optimizes the browsing path but also improves the accuracy of recommended content, ensuring that the final presented content better matches the user's actual needs.

[0239] like Figure 10 As shown, this is a schematic diagram of a content recommendation interface in an embodiment of this application. Figure 10 The interface shown is 101. Figure 8 The interface 82 shown displays a content preview card, while interface 102 is a content recommendation interface that appears after the preset search conditions are met. For example, interface 102 displays four recommended contents related to badminton serve, namely recommended content 1, recommended content 2, recommended content 3 and recommended content 4.

[0240] Optionally, when redirected to the content recommendation interface, a refresh notification message similar to a refresh can also be displayed on the content recommendation interface. Specifically, it can be displayed at the top of the content recommendation interface or in other prominent locations to ensure that the user can see it at a glance.

[0241] For example: We've tailored 50 pieces of content just for you, or: Based on your interests, we've updated our recommended content. This refresh notification doesn't require object manipulation; its main purpose is to inform the user that the system has dynamically updated the recommended content based on their browsing goals and behavior.

[0242] Optionally, during the browsing process, prompts can be provided on how to monitor and evaluate progress towards goals. These progress tracking tips include "Set weekly study time" and "Record daily reading volume," helping users develop feasible plans and ensuring that goal setting is not just a short-term behavior but a long-term habit. Furthermore, positive language can be used to encourage users to set and pursue goals, such as "Today's efforts lead to tomorrow's success!" and "Every click is a step forward," thereby enhancing their motivation and confidence to achieve their goals. Additionally, popular or recommended content tags related to the preset browsing goals can be displayed, such as "Technology Trends," "Design Thinking," and "Relaxing Music," providing immediate content direction and making it easier for users to find areas of interest while increasing the possibility of exploration.

[0243] In this embodiment of the application, after S23, the user can browse recommended content, and of course, can also browse other content. In order to ensure that the content browsed by the user conforms to the set goal as much as possible, the system can further refine the user's browsing goal through real-time goal conformity detection during the user's browsing process, improve the accuracy of recommended content, and make the recommendation results more in line with the user's current needs.

[0244] Specifically, target conformity detection refers to the system (which can be a client or a server) using a series of evaluation indicators to assess in real time whether the currently viewed content conforms to the set target during the browsing process.

[0245] Optionally, different evaluation indicators can be set for different types of goals (such as "learning new knowledge", "entertainment and relaxation" or "finding inspiration"). The system can also detect the user's interactive behavior and emotional reactions in real time. Through this information, the system can determine the matching degree between the content and the goal, assess whether the content currently being viewed by the user meets the user's set goal, and provide timely feedback through a gentle reminder window to help the user adjust their browsing direction.

[0246] This approach ensures that recommended content always aligns with the user's needs, improving user experience and goal achievement rates, and making recommendations more accurate and personalized. For example, if it is determined that a user's behavior deviates from the predetermined path, the user can be promptly alerted to adjust, ensuring the smooth achievement of the goal.

[0247] For example, for the goal of "learning new knowledge", the system can monitor the knowledge depth and professionalism of the page content, and evaluate it in combination with the user's interactive behavior (such as reading time, note-taking, bookmarking, etc.).

[0248] Knowledge depth refers to whether the page content covers in-depth professional knowledge. Professionalism refers to whether the content comes from authoritative sources and has high academic or practical value. Dwell time refers to how long users spend reading the page; a longer dwell time usually indicates that the content is more attractive and relevant.

[0249] For the goal of "entertainment and relaxation," the system pays more attention to the emotional tone and entertainment value of the page content, while also monitoring the emotional reactions of the users.

[0250] Emotional tone refers to whether the content is positive and uplifting, and whether it brings a sense of pleasure. Entertainment value refers to whether the content is interesting and engaging, such as videos and games. Furthermore, behaviors such as liking, sharing, and commenting can be used to assess the emotional state and satisfaction of users.

[0251] For the goal of "finding inspiration," the system can set specific evaluation metrics, such as creative stimulation and emotional resonance, to monitor whether the currently viewed content meets the user's inspirational needs. These metrics aim to capture creative triggers and the user's emotional responses, ensuring that recommended content effectively promotes the user's creativity and innovative thinking.

[0252] The level of creative inspiration can be assessed from the following angles: whether the content covers multiple fields or perspectives, providing a wide range of sources of inspiration; whether the page content contains novel and unique viewpoints or ideas that can break conventional thinking patterns; and whether the content involves the cross-integration of different fields, such as art and technology, design and business, to stimulate multi-dimensional thinking.

[0253] Emotional resonance can be assessed from several angles, including whether the content has strong emotional expression and can evoke resonance and emotional fluctuations in users; whether the page content conveys information through captivating stories or cases to enhance user immersion; and whether multimedia elements such as images and videos have strong visual appeal, quickly grabbing users' attention and inspiring them.

[0254] It should be noted that the evaluation indicators or methods related to target conformity detection listed above are just simple examples. There can be other evaluation indicators or methods. For example, the system can also evaluate the relevance between the current browsing content and the target browsing object based on preset relevance algorithms (such as keyword matching, object browsing behavior, etc.). The explanation of target conformity detection here is just a simple example. It will be further elaborated on the server side, and will not be repeated here.

[0255] The application of target conformity testing is explained in detail below:

[0256] This application takes into account that during the browsing process, the current object browsing target may be interrupted for various reasons, or the current object browsing target may not be completed in this session and needs to be continued in a later session. In order to maintain continuity and consistency of target during object browsing, an optional implementation method is as follows:

[0257] After the preset reminder conditions are met, a feedback prompt is displayed. This feedback prompt is used to remind the user to continue browsing the previous target (i.e., the original target). Specifically, it can directly remind the user of the original target, or it can ask the user whether to continue to the original target. If the user decides to continue browsing the previous target, recommended content related to the user's browsing target will be displayed again on the client side.

[0258] In this embodiment, the feedback prompt can be presented immediately after the preset reminder conditions are met, or it can be presented some time after the preset reminder conditions are met. Specifically, if the user has not closed the client, the feedback prompt can be presented on the interface currently being viewed by the user after the preset reminder conditions are met; if the user has closed the client, the feedback prompt can be presented when the user reopens the client after the preset reminder conditions are met; and so on.

[0259] Optionally, preset reminder conditions include, but are not limited to, at least one of the following:

[0260] Preset reminder condition 1: The object will resume browsing after the browsing target is interrupted.

[0261] For example, when an object resumes browsing a target after a period of interruption, the system will ask it to continue browsing the target as before.

[0262] Taking a browser as an example, the system can dynamically adjust the timing and method of displaying feedback prompts based on the user's current state and behavior to ensure the user receives the prompt at the most appropriate time. For instance, if the user hasn't closed the browser and the preset reminder conditions are met, the system can immediately display the feedback prompt on the currently viewed interface. Alternatively, to avoid frequently disturbing the user, the system can set a waiting period (e.g., 5 minutes) after detecting an interruption. If the user doesn't continue browsing during the waiting period, the feedback prompt will be displayed the next time the user opens the browser or returns to the page. Furthermore, if the user closes the browser, the system will display the feedback prompt on the currently viewed interface when the user reopens the browser, provided the preset reminder conditions are met. This flexible prompting mechanism ensures the user receives the prompt at the most appropriate time, avoids frequent interruptions, and helps the user maintain focus on the main objective, improving goal achievement rate and user experience.

[0263] In practical applications, there are many situations where browsing a target is interrupted by using an object. To ensure continuity and consistency of the target during the browsing process, the system provides various intelligent detection and prompting mechanisms for different types of interruptions, including but not limited to the following:

[0264] (1) Interruption caused by external interference. Specifically, this refers to the sudden interruption of the user's browsing behavior due to external factors (such as telephone calls, pop-up ads, etc.), causing the user to temporarily leave or forget the current target.

[0265] In one optional implementation, when external interference (such as a phone interruption, clicking on a pop-up ad, etc.) is detected, a certain waiting period (e.g., 5 minutes) can be set. If the user continues browsing during the waiting period, the target conformity detection is automatically resumed; if there is no further browsing behavior during the waiting period, a feedback prompt will be sent to the user the next time they open the browser (e.g., a browser), asking whether they want to continue with the original target.

[0266] For example, if a user is browsing recommended content on the content recommendation interface and is suddenly interrupted by a phone call, forgetting their target, the system sets a 5-minute waiting period after the call ends. If the user does not continue browsing, they will be asked whether to continue with the original target the next time they open the browser.

[0267] For example, when a user is browsing recommended content on a content recommendation interface and is drawn away from their target by a pop-up ad, the system analyzes the user's click behavior and dwell time to determine if the ad caused the user to deviate from their target. Once the user returns to the main page, they are immediately reminded of their original target, and a new target consistency check is performed.

[0268] In another optional implementation, if the user leaves due to an emergency (such as remembering another urgent matter while browsing), the system records the user's browsing behavior and page state before leaving. Once the user returns to the browser, the system immediately reminds the user of the original target through feedback information and re-performs the target conformity check to ensure that the user does not deviate from the target due to external interference.

[0269] (2) Interruptions caused by multitasking. Specifically, this refers to the user suddenly engaging in other activities during browsing, such as searching for relevant information, switching to other applications to take notes or reply to messages, resulting in a temporary departure from the current browsing task.

[0270] In one alternative implementation, when a user searches within a browser, the system analyzes the relevance of the search keywords to the original target. If the relevance is high, it can be assumed that the user is still exploring the target. At this point, feedback prompts can be displayed at an appropriate location on the search results page to remind the user of the original target, and the target relevance detection metrics can be adjusted based on the search keywords. For example, if a user searches for keywords in a specific field under the target of "learning new knowledge," the weighting of knowledge depth and expertise in that field can be increased for more accurate target relevance detection.

[0271] For example, when a user is learning badminton serving techniques based on recommended content, and searches for "serving skills videos," the system analyzes the keywords and confirms that the relevance is high, prompting the user to continue with the original goal and adjusting the detection indicators.

[0272] In another alternative implementation, when the system detects that the user has switched to another application (such as taking notes or replying to messages), a gentle prompt window will pop up when the user returns to the browser, asking whether to continue with the original target, and adjusting the detection strategy based on the user's choice. Simultaneously, the system records the user's browsing behavior and page state before leaving to quickly resume target compliance detection and ensure a seamless transition.

[0273] For example, when an user is learning badminton serve techniques based on recommended content, they can switch to a note-taking app to record key points. When they return to the browser, the system will prompt them to continue with the original goal and quickly restore their previous browsing state.

[0274] (3) Interruptions caused by technical issues. Specifically, this refers to interruptions caused by technical problems such as browser lag or slow loading, which cause users to lose patience and abandon the current browsing task.

[0275] In one alternative implementation, once browser lag or slow loading is detected, the system records the current state and, once normal operation is restored, immediately reminds the user of the original target through a feedback message. For example: "Your browser has returned to normal; we recommend continuing to learn badminton serve movements." The system also re-performs a target conformity check to ensure the user does not deviate from the target due to technical issues.

[0276] Additionally, it can provide temporary offline resources or cached content to reduce user waiting time and improve user experience.

[0277] It should be noted that the interrupt handling mechanisms listed above are only simple examples. Other interrupt handling mechanisms are also applicable to the embodiments of this application, and will not be described in detail here.

[0278] Through the interruption handling mechanisms listed above, the system can intelligently identify and respond to various interruption situations, ensuring that users can seamlessly resume their previous tasks when browsing again, avoiding deviation from the goal due to interruption. This approach not only improves user experience and goal achievement rate but also significantly enhances the system's flexibility and intelligence.

[0279] Preset reminder condition two: The preset browsing time related to the object browsing target has been reached, but the object browsing target has not yet been completed.

[0280] The preset browsing time refers to the time limit set by the user to complete a specific browsing goal. Whether the goal is achieved is determined by the system based on the user's browsing behavior and test evaluation results, etc., to see if the user has met the expected learning or task requirements. In this embodiment, if the user reaches the preset browsing time related to the browsing goal but has not yet completed the browsing goal, the system will ask the user whether to continue through feedback prompts to ensure that the goal is ultimately achieved.

[0281] For example, if the browsing goal set by the user is "learn badminton serving motion for 1 hour and understand the key points of serving", the preset browsing time is 1 hour. After 1 hour, if the user has not fully understood the key points of serving, it can be determined that the browsing goal has not been completed, and the system will prompt it with feedback information to ask whether to continue.

[0282] This approach ensures that users can seamlessly resume their browsing from where they left off, preventing them from straying from their goals due to interruptions and improving user experience and goal achievement rates. Furthermore, this mechanism helps users maintain focus on the main objectives, optimizes time and content management, and ensures that users efficiently achieve their set goals.

[0283] In this embodiment, the system can also intelligently allocate and manage browsing time to prevent users from becoming overly engrossed in low-value content and wasting too much time. When a user's browsing time approaches the set limit, the system will issue a reminder or warning, suggesting that the user rationally plan the remaining time to ensure efficient achievement of the predetermined goals. An optional implementation is as follows:

[0284] After detecting that the user has been continuously browsing secondary content for a preset period of time, a browsing prompt message is displayed. The browsing prompt message is used to indicate that the secondary content is not related to the user's browsing target. Secondary content refers to content whose relevance to the user's browsing target is lower than a preset relevance threshold.

[0285] Specifically, during the browsing process, the system can monitor the user's browsing behavior in real time, assess whether the content currently being browsed aligns with the user's set goals (for specific assessment methods, please refer to the server-side instructions for goal conformity detection mentioned above, which will not be repeated here), and record the relevance between the browsed content and the user's browsing goals. If the relevance of the currently browsed content is lower than a preset relevance threshold (e.g., 50%), it is marked as secondary content, i.e., low-value content. For specific assessment methods, please refer to the server-side instructions, which will not be repeated here.

[0286] The system (client or server) continuously monitors the user's browsing time on secondary content. When it detects that the user has been browsing secondary content for a preset time (e.g., 10 minutes), it triggers a prompt mechanism and presents browsing prompt information to the user through the client to remind the user that the content currently being browsed may be irrelevant to the user's browsing target. In addition, it can also suggest that the user return to content related to the user's browsing target.

[0287] Specifically, browsing prompts can include various types of content such as text, images, and voice. Text can provide intuitive and clear reminders, such as "The content you are currently browsing is not relevant to your target; we suggest you return." Images attract users' attention through icons or animations, enhancing the visual cue effect. Voice conveys information through gentle tones or broadcasts, without interrupting the user's visual focus.

[0288] These different types of content can be flexibly combined according to specific application scenarios to ensure that prompts are effectively conveyed without overly disturbing users, thereby improving the overall user experience.

[0289] Optionally, the browsing prompts may take one or more of the following forms:

[0290] Pop-ups, notification messages, in-interface prompts, gentle sound alerts, gradient or animated prompts, progress bar prompts, and floating buttons.

[0291] For example, a small window could pop up in the center or corner of the screen, displaying a browsing prompt: "The content you are currently viewing is irrelevant to your goal. We suggest you return to content related to 'learning badminton serve techniques'." This pop-up prompt is intuitive and eye-catching, easily attracting the user's attention.

[0292] Alternatively, a short message can be displayed in the notification bar at the top or bottom of the screen: "Tip: The current content is not highly relevant to the target; adjustments are recommended." This notification method does not interrupt the user's current operation, and the user can choose when to view it.

[0293] Alternatively, a semi-transparent tooltip can be displayed in a fixed location on the current page (such as the bottom right corner), showing "Tip: You may be viewing content unrelated to your target." This type of in-interface tooltip does not affect the page layout, and users can continue browsing while seeing the tooltip.

[0294] Alternatively, a gentle voice or sound effect can be used to remind the user, while a short message can be displayed on the screen: "Ding, we suggest you return to content related to your target." This type of sound reminder is suitable for scenarios that require maintaining visual focus, as the sound is gentler.

[0295] Alternatively, use color changes or subtle animation effects to attract the user's attention, such as the page edge gradually turning red or flashing, accompanied by the prompt text "We suggest you pay attention to the relevant content." This kind of gradient color or animation prompt is visually appealing but not jarring, and users are likely to notice it.

[0296] Alternatively, a progress bar can be displayed at the top or bottom of the page. As the user browses secondary content, the progress bar gradually fills, and when it reaches a certain threshold (such as being full), a prompt appears, such as "You have spent too much time on irrelevant content; we suggest you adjust your schedule." This progress bar approach provides intuitive time management feedback, helping users become aware of the passage of time.

[0297] Alternatively, a floating button can be displayed in the corner of the page. Clicking it will display detailed information, such as a "?", followed by the message "The content you are currently viewing is irrelevant to your target; we suggest you return." This floating button method does not take up much space, and users can choose whether to view the details.

[0298] It should be noted that the methods listed above can also be used in combination, which will not be elaborated on here. Furthermore, the content and format of the browsing prompts listed above are merely examples; this article does not impose specific limitations on them, and they can be optimized based on user experience design.

[0299] In addition, it should be noted that preset parameters such as preset duration and preset relevance threshold can be flexibly adjusted according to different application scenarios, and will not be elaborated on here.

[0300] like Figure 11 The diagram illustrates a pop-up browsing prompt message in an embodiment of this application. Assume a user sets a browsing goal of "learning badminton serve techniques for 1 hour" and begins browsing tutorials and videos related to badminton serves. During browsing, the user accidentally clicks on an entertainment news link unrelated to badminton and continues browsing for over 10 minutes. The system detects that the content the user is browsing has extremely low relevance to the goal of "learning badminton serve techniques," falling below a preset relevance threshold, and thus prompts: "The content you are currently browsing seems somewhat inconsistent with your goal. Take a break and continue focusing on your goal~" Figure 11 As shown in the dashed box 111.

[0301] Of course, if the user wants to continue viewing this entertainment news, they can do so through... Figure 11 The "×" in the dashed box 114 closes the pop-up window.

[0302] In the above implementation, timely reminders help users maintain focus on the main objective and avoid wasting time on irrelevant content. This approach not only improves time utilization but also helps users stay focused and avoid excessive immersion in irrelevant information, thereby enhancing concentration and time management efficiency. Furthermore, gentle prompts do not interrupt the user's browsing experience but provide helpful feedback, assisting them in achieving their goals more effectively. Overall, this not only enhances the user experience but also effectively improves goal completion efficiency.

[0303] It should be noted that the above embodiments introduce a real-time reminder mechanism for low-value content. Specifically, when the system detects that a user is continuously browsing low-value content that does not align with their set goals over a period of time, a gentle reminder window will pop up promptly, indicating that the current content may not meet their set goals. Furthermore, this application also supports providing relevant suggestions or guiding the user to rethink and adjust their goals while issuing the reminder.

[0304] The method for adjusting the target will be explained below:

[0305] Optionally, to further help users optimize their browsing path and ensure they always focus on the set target, the browsing prompts in this application can also include a target adjustment control; in this way, the system not only reminds users that the currently viewed content is irrelevant to the target, but also provides a convenient way for users to quickly adjust the target.

[0306] Based on this, an alternative implementation is that the client responds to a third setting operation triggered by the user object through the target adjustment control, determines the adjusted object browsing target input by the user object, and updates the content recommendation interface according to the adjusted object browsing target.

[0307] There are many ways to trigger a third-party setting operation by adjusting the target control using an object, such as clicking or long-pressing, which will not be elaborated here. Figure 11 The “adjustment target” in the dashed box 112 is a “target adjustment control” in this application. Clicking the control with an object will trigger a third setting operation.

[0308] In this embodiment, after the object triggers the third setting operation, the previously set object browsing target can be adjusted directly through voice input, keyboard input, or other methods. In this mode, the client responds to the third setting operation triggered by the object through the target adjustment control, thereby directly detecting the adjusted object browsing target input by the object. Therefore, after the object adjusts the browsing target, the content recommendation interface is updated based on the adjusted object browsing target, displaying new recommended content related to the adjusted object browsing target.

[0309] Optionally, after the user triggers a third setting operation, the client can also respond to the user's third setting operation triggered by the target adjustment control, return to the target setting interface, and display adjustment prompt information in the target setting interface; this adjustment prompt information is used to guide the user to adjust the browsing target; thus, after the user adjusts the browsing target, the content recommendation interface is updated according to the adjusted browsing target, and new recommended content related to the adjusted browsing target is displayed.

[0310] Similar to browsing prompts, the content of these adjustment prompts can also include various types such as text, images, and voice, which will not be elaborated on here.

[0311] For example, when a user triggers a third-party setting operation through a goal adjustment control (such as a button, icon, or text link), the client responds and presents a goal setting interface. In this interface, the system displays adjustment prompts to guide the user on how to optimize and adjust their current browsing goal. For example, "Your current goal is 'learn badminton serve,' we suggest refining it to 'understand the key points of the serve' or 'master practical serve techniques.'" The user adjusts their browsing goal based on these prompts, selecting a more specific or suitable content direction, such as refining a broad learning goal to a specific skill point. Once the user completes the goal adjustment, the system re-searches and filters relevant content based on the new browsing goal and updates the content recommendation interface in real time, ensuring that the recommended content is highly relevant to the adjusted goal and providing a more accurate and personalized browsing experience.

[0312] In the above implementation, by adding a target adjustment control to the browsing prompts, users can immediately adjust their browsing goals upon receiving the prompt without interrupting their current operation, enhancing interactivity and flexibility. Furthermore, the adjustment prompts guide users to optimize their goal settings, ensuring the new goals are specific and feasible, improving accuracy. The system updates the content recommendation interface in real time based on the adjusted goals, further ensuring that recommended content is always highly relevant to the user's needs and avoiding interference from irrelevant content. This approach not only helps users maintain focus and manage their time efficiently but also improves the overall user experience, reducing frustration caused by deviating from the target, thus achieving a more efficient and targeted browsing experience.

[0313] The following is an explanation of the relevant content of the suggestion:

[0314] Optionally, to further enhance the browsing experience and provide immediate content selection, the browsing prompts in this application may also present at least one content summary card, each content summary card corresponding to a recommended piece of content; these content summary cards are generated based on recommended content related to the browsing goals of the user; the content summary cards are generated based on recommended content related to the browsing goals of the user and are used to provide the user with an intuitive and specific selection reference.

[0315] Based on this, an optional implementation is as follows: In response to the user's selection operation of a target content summary card in at least one content summary card, the client jumps to the content details interface of the recommended content corresponding to the target content summary card.

[0316] In this embodiment, the content summary card is similar to the content preview card. It is automatically generated based on several recommended content items that have been searched. In addition to displaying the visual information of the content (such as the content cover image), it can also provide a brief text description (such as a brief title) and support the link function, allowing users to click to enter the content details page to obtain more information.

[0317] For example, if the system detects that a user has been continuously browsing secondary content for a preset period of time, it will display a browsing prompt message reminding the user that the content is irrelevant to the target. The browsing prompt message will include at least one content summary card, and each content summary card will correspond to a recommended piece of content that is highly relevant to the user's browsing target. For example, under the target "Learn badminton serve," content summary cards such as "Basic serve technique video" and "Serve essentials illustrated explanation" will be displayed.

[0318] Users can select a specific content summary card based on their interests, such as clicking "Basic Serving Techniques Video". The system will then respond to the user's selection and directly jump to the recommended content details page corresponding to that card, such as playing a video or opening a detailed text and image analysis page.

[0319] The above implementation not only helps users maintain focus on their primary goals but also improves user experience and content acquisition efficiency through real-time content recommendations. In other words, this approach allows users to immediately view recommended content upon receiving a notification and provides a convenient path for in-depth browsing and learning, ensuring that users remain focused on their set objectives.

[0320] In this embodiment, the user can immediately view relevant recommended content upon receiving a prompt. To further expand the content browsing and recommendation mechanism and ensure that the user can easily return to or switch to other recommended content after viewing the specific content details, this application also proposes the following method:

[0321] After navigating to the content details page of the recommended content corresponding to the target content summary card, the user can trigger a return operation to return to the previously viewed content recommendation page related to the target. One possible implementation is that the client responds to the return operation triggered by the user through the content details page and returns to the content recommendation page.

[0322] Specifically, a "back" button can be set in the content details interface. When users finish browsing the content details interface, decide not to continue viewing the current content, or wish to view more related content, they can return to the content recommendation interface by clicking the "back" button or similar operation. This allows users to re-evaluate other recommended content, maintaining browsing flexibility and diversity.

[0323] For example, if a user selects the "Basic Serving Techniques Video" summary card, the system will redirect to the video's details page. After watching the video, the user can click the "Back" button, and the system will return to the content recommendation page under the "Learn Badminton Serving Techniques" goal, displaying more recommended content related to "Learn Badminton Serving Techniques," thus allowing the user to continue browsing towards the "Learn Badminton Serving Techniques" browsing goal.

[0324] After navigating to the content details page of the recommended content corresponding to the target content summary card, the user can also trigger a switching operation to switch to other recommended content. One possible implementation is as follows:

[0325] The client responds to the switching operation triggered by the user through the content details interface, and displays the content details interface of other recommended content.

[0326] Specifically, a "Switch" button can be set in the content details interface. When a user finishes browsing the content details interface, decides not to continue viewing the current content, or wants to view more related content, they can directly jump to the content details interface of other recommended content by clicking the "Switch" button or similar operations (such as swiping up, down, left, right, etc.). This method provides a seamless content switching experience, helping users find content that meets their needs more efficiently.

[0327] For example, if a user selects the "Basic Serving Techniques Video" summary card, the system will jump to the video's details page. After watching the video, or if the user finds something more interesting, such as "Advanced Serving Techniques Illustrated Explanation," they can click the "Switch" button, and the system will directly jump to the details page of that illustrated explanation.

[0328] Both of these implementation methods significantly improve the browsing experience and efficiency. The "back" operation allows users to easily return to the content recommendation interface, re-evaluate other recommended content, and maintain flexibility. The "switch" operation allows users to directly jump to the details of other recommended content from the details interface, providing a seamless switching experience. These two mechanisms enhance the continuity and convenience of browsing, ensuring users always focus on their goals, reducing operational steps, and improving overall satisfaction and task completion rates.

[0329] The following illustrations, with reference to the attached diagrams, provide examples of the real-time alert mechanism for the low-value content listed above.

[0330] like Figure 12 As shown, this is a schematic diagram of the first type of browsing prompt information in an embodiment of this application. For example, after setting a clear goal of "learning badminton serving for 1 hour and understanding the key points of serving," the user switches to other tabs, such as... Figure 12In interface 121, switch to the tab corresponding to "Screening Room" as shown in interface 122. If you browse irrelevant content in interface 122 for a long time, the system will remind you: "The content you are currently browsing does not seem to be consistent with the goal. Take a break and continue to move towards the goal~", as shown in 1231 in interface 123. In addition, it will also provide some relevant suggestions (implemented through content summary cards) or guide users to rethink and adjust their goals (implemented through goal adjustment controls).

[0331] like Figure 13 As shown, this is a schematic diagram of the second type of browsing prompt information in an embodiment of this application. For example, after setting a vague goal of "entertainment and relaxation," the user switches to other tabs, such as... Figure 13 In interface 131, if you switch to the tab corresponding to "File" (as shown in interface 132) and stay on a boring technical article page for a long time, the system will remind you: "The content you are currently browsing does not seem to be consistent with the goal. Take a break and continue to move towards the goal~", as shown in 1331 of interface 133. In addition, it will also provide some relevant suggestions (implemented through content summary cards) or guide users to rethink and adjust their goals (implemented through goal adjustment controls).

[0332] Based on the above Figure 12 or Figure 13 Taking the browsing prompts shown as an example, if a user selects to view related content, the system can automatically open a new tab, display recommended content, and indicate its relevance to the target on the page, making it clear to the user at a glance. Simultaneously, the system can continue to monitor the user's browsing behavior to ensure that the user continues to browse around the target.

[0333] If the user chooses to reset the goal, the system can guide the user to adjust the goal and provide some examples and guidance questions to help the user clarify their needs. The adjusted goal can take effect immediately, and the system will re-perform the goal compliance check and reminder mechanism.

[0334] If the user chooses to continue browsing (ignore the reminder), the system can perform another check after a period of time. If the user is still browsing content that does not match the target, a reminder can be issued again, but the frequency of reminders can be appropriately reduced to avoid interfering with the user.

[0335] In addition, this application also takes into account the user's time management and efficiency optimization, supports the system to automatically generate detailed content browsing reports, show the user's activities in various time periods, and provide personalized suggestions.

[0336] An optional implementation method is as follows:

[0337] The client presents a viewing control for a content browsing report to the user; wherein, the content browsing report is generated based on the user's browsing data within a specified time period; when the user views the report, the client responds to the report viewing operation triggered by the user through the viewing control and displays the content browsing report.

[0338] In this embodiment, the system records the browsing behavior and activities of the user within different time periods, generates a detailed content browsing report, and presents corresponding viewing controls to the user. This content browsing report is a comprehensive analysis report generated based on the browsing goals set by the user in the client (such as a browser) and the actual browsing behavior within a specified time period. This report helps users understand whether their browsing activities within a specified time period are consistent with their set browsing goals and provides detailed statistics and suggestions to optimize future browsing behavior.

[0339] The specified time refers to a specific browsing period preset by the user or system to generate a content browsing report. It can be a fixed time period (e.g., one day, one week) or a continuous browsing session (e.g., one hour of continuous browsing). By specifying a time, the system can focus on the user's behavior within that time period, providing more accurate analysis. Several examples are listed below:

[0340] (1) Daily Report: If a specific time is set for each day, the system will generate a content browsing report every night to summarize the browsing behavior of the day.

[0341] (2) Continuous browsing session: Users can select a specific continuous browsing period, such as "from 7 pm to 8 pm". The system will record all browsing activities during this period, generate a detailed content browsing report, and analyze the user's interests and behavioral patterns.

[0342] (3) Single long-term browsing: For example, if the user browses for learning for up to 3 hours, the system will generate a special content browsing report based on the behavioral data during these 3 hours, evaluate the learning effect and recommend relevant resources.

[0343] In this way, content browsing reports can provide in-depth analysis for specific time periods, helping users better understand their browsing habits and needs.

[0344] It should be noted that the above-listed methods for setting the specified time are just simple examples. Other methods for specifying time are also applicable to the embodiments of this application, and will not be described in detail here.

[0345] Optionally, you can view the specific rendering method of the control as follows:

[0346] Regarding the timing of presentation, the system can present the report viewing control in a timely manner when the goal is achieved, the current browsing ends, or the user re-enters the browser, reminding the user to view the detailed browsing behavior report.

[0347] For example, when a user completes a set goal, the system automatically pops up a gentle prompt window informing the user that they can view a detailed browsing behavior report and presenting a viewing control. Another example is when a user ends the current browsing session (e.g., closes the browser or exits the application), the system can provide an option to view the report (e.g., presenting a viewing control) on the end page or confirmation dialog box. Yet another example is when a user reopens the browser or re-enters the application, the system can prominently prompt the user to view the latest browsing behavior report on the welcome screen or homepage, presenting a viewing control.

[0348] Regarding its display location, this view control can appear in key locations such as the target setting interface, the current browsing interface, and the content recommendation interface, making it convenient for users to access it at any time.

[0349] For example, when setting or adjusting goals for an object, the system can provide a "View Report" button (a viewing control) on the interface, allowing the user to easily review previous browsing behavior. Another example is that during browsing, the system can provide a small icon or button (a viewing control) at the top of the page or in the sidebar, which the user can click to view a real-time updated browsing behavior report. Yet another example is that in a prominent location on the content recommendation interface (such as a top banner or bottom bar), the system can place a "Browsing Behavior Report" entry (yet another viewing control) to help users understand their browsing progress at any time.

[0350] Regarding the specific presentation method, gentle prompt windows and fixed controls, combined with visual effects and personalized recommendations, can be used to ensure that users can easily and user-friendly obtain the report.

[0351] For example, pop-ups or floating notifications can gently remind users that new browsing behavior reports are available, avoiding disruption to their normal operations. Another example is setting fixed viewing controls (such as buttons or icons) on various interfaces, ensuring users can access reports at any time without additional searching. Specific animation effects such as fade-in / fade-out and sliding can be used to attract user attention while maintaining a clean and user-friendly interface. Furthermore, based on the content of the browsing behavior reports, the system can provide personalized recommendations or improvement suggestions to help users better achieve their goals. This approach not only ensures users receive detailed browsing behavior reports promptly but also improves user experience and goal achievement rates, enabling users to manage their browsing activities more efficiently.

[0352] It should be noted that the above-listed display control presentation methods are just simple examples. Other presentation methods are also applicable to the embodiments of this application, and will not be described in detail here.

[0353] like Figure 14 As shown, this is a schematic diagram of a viewing control in an embodiment of this application. Specifically, as... Figure 14 The "View detailed content browsing report>" control in dashed box 142 is presented in the goal setting interface. In addition, the goal setting interface also presents prompts indicating that the goal has been completed, such as "Congratulations! You have completed today's goal" in dashed box 141.

[0354] When an object views a report using a view control, the client-side can display the specific content of the content browsing report. Optionally, the content of the content browsing report includes, but is not limited to, at least one of the following:

[0355] Use the browsing activity of an object within a specified time period; suggestions for subsequent browsing related to the object's browsing target.

[0356] For example, when an object views a report using a view control, the client-side can display the specific content of the content browsing report. This report not only provides detailed browsing information but also includes suggestions for future browsing, helping the user better understand and plan future browsing behavior. The following is a description of the specific content and its related information:

[0357] In the embodiments of this application, the browsing status of the user within a specified time period includes, but is not limited to, the browsing status of the user in each time period within the specified time period, such as time allocation information, target progress information and report summary, browsing details, etc.

[0358] The time allocation information displays the user's total browsing time and browsing activity within a specified period (such as a day or a single continuous browsing session), helping them understand their performance at each stage. For example, it can display the browsing time, number of websites visited, and number of interactions for each time period by hour.

[0359] The goal progress information is used to display the user's progress in viewing the defined goal across different time periods, in conjunction with goal completion status. For example, the system can indicate whether the user has achieved the expected relaxation effect or learning progress within a certain time period. Optionally, this goal progress information can be in the form of a goal completion progress bar, such as... Figure 15 As shown in section 1511 of 151.

[0360] The report summary is a comprehensive evaluation of the user's browsing activities within a specified time period, such as summarizing the core points learned by the user and pushing the highlights of the user's attention during the process.

[0361] The browsing details record the user's specific browsing behavior and corresponding statistics within a specified time period, including but not limited to each page visited, dwell time, number of interactions (such as clicks, likes, comments, etc.), and content type (such as news, videos).

[0362] Subsequent browsing suggestions related to the user's browsing goals can be generated based on the progress towards those goals. For example, if the user fails to fully achieve the set goals, the system will provide improvement suggestions. For instance, for "relax and have fun," the system might recommend some lighter recreational activities; for "learn new knowledge," it might suggest further exploration of related topics or participation in online discussions. If the user successfully achieves the goals, the system will provide positive feedback and recommend higher-level content or activities. For instance, for "relax and have fun," it might recommend high-quality recreational activities to reinforce the relaxation experience; for "learn new knowledge," it might push comprehensive knowledge challenges or in-depth discussion topics to solidify learning outcomes.

[0363] If the user also participates in interactive tests based on target detection content within a specified time period, statistical information about the interactive tests can also be recorded in the content browsing report, including but not limited to accuracy, sentiment score, satisfaction, etc. Figure 15 As shown in the interactive test section of page 153.

[0364] Assuming the day ends, the system automatically generates a detailed content browsing report. The report displays the user's activities over various time periods, including but not limited to the categories of topics browsed, the percentage of time spent on each topic, and the degree of goal completion.

[0365] Optionally, this application supports adjusting subsequent recommended content based on the browsing topic categories and goal completion rates in the content browsing report. If a user spends a considerable amount of time learning about a specific topic under the "Learn New Knowledge" goal and achieves a high goal completion rate (or simply "completion rate"), further in-depth content on that topic or knowledge in related fields can be recommended to promote sustained learning. For example, if a user demonstrates outstanding performance in the field of science and technology, cutting-edge scientific research papers or professional lectures can be recommended.

[0366] Optionally, when users fail to achieve their learning goals within a certain period, motivational content can be pushed to them, such as case studies of successful learners or articles on learning techniques, to stimulate their learning motivation. Simultaneously, recommendations for learning tools can be added to the content browsing report. For example, if users need to take notes during their learning process, the browser's note-taking function can be recommended, along with tutorials and case studies to help them better utilize their browser for learning.

[0367] For the goal of "learning new knowledge," the content browsing report can display the number of times the user watched related videos, read related articles, and viewed unrelated videos, such as... Figure 15 As shown in the browsing details section of page 153, related time allocations can also be displayed, such as... Figure 15 As shown in section 1513 of 151, you can also summarize the core points learned from using the object and highlight the moments of focus during the process, such as... Figure 15 As shown in section 152 (Report Summary).

[0368] Optionally, this application also supports recommending similar content formats or themes based on the user's peak attention moments in the report. For example, if the user is highly focused while watching an engaging science video, more learning resources of the same style can be recommended to enhance the user's learning interest and concentration.

[0369] Furthermore, the content browsing report in this embodiment not only presents data but also analyzes the user's time utilization, pointing out advantages and disadvantages. For example, it may detect that the user was browsing irrelevant videos and was interrupted by a pop-up message, thus halting their original operation. Figure 15 As shown in section 1513 of 151.

[0370] Optionally, for irrelevant content that users spend a significant amount of time on according to the content browsing report, their characteristics and the reasons why users are attracted to them can be analyzed. Similar elements can then be appropriately incorporated into recommendations, but guided towards areas relevant to the learning objectives. For example, if users are frequently drawn to entertainment news during their studies, interesting anecdotes or innovative stories in the technology field can be recommended, satisfying their interests while remaining relevant to their learning goals.

[0371] At the same time, based on the user's historical reports and behavioral patterns, the system can provide personalized suggestions to the user, such as... Figure 15 As shown in section 1514 of 151, this helps users better plan their future browsing goals and time allocation. For example, in serving techniques, pay attention to the height of the serve over the net.

[0372] Optionally, when a user starts a new day or a new learning phase, targeted reminders and suggestions can be pushed based on the strengths and weaknesses reviewed in the historical content review report. For example, if the user was frequently distracted by pop-up messages during the previous day's study, they can be reminded to turn off unnecessary notifications and focus on learning at the start. Based on the user's behavioral patterns, short and interesting learning content can be pushed during times when the user is typically easily distracted to maintain continuity in learning. For instance, if the user is prone to fatigue and distraction in the afternoon, lighthearted knowledge flashcards or short videos can be pushed.

[0373] The above implementation not only displays detailed browsing information and subsequent browsing suggestions, but also closely links goal progress information and report summaries to this information, helping users fully understand their browsing behavior and goal achievement, thereby optimizing future browsing plans. With these detailed reports, the system not only enables users to clearly understand their browsing and learning progress, but also provides practical improvement suggestions, helping them better manage time and achieve goals. Ultimately, these features significantly improve users' overall efficiency and satisfaction, helping them use their time more effectively and achieve personal goals.

[0374] Optionally, to further enhance the browsing experience and goal achievement efficiency, this application not only provides a detailed content browsing report but also introduces tool controls to enhance interactivity and convenience during the browsing process. One optional implementation is as follows:

[0375] The content browsing report in this application may also include tool controls, which users can use to learn about auxiliary tools. Optionally, in response to a user's tool viewing operation triggered by the tool control, the client presents a tutorial on using the auxiliary tools related to the user's browsing target.

[0376] The tool control can be displayed anywhere in the content browsing report, specifically in a prominent location; this document does not impose any specific limitations. This auxiliary tool is used to assist the user in browsing related to the browsing target, helping them better understand and achieve their goals.

[0377] Specifically, the content browsing report interface provides a clear tool control, such as a "View Accessibility Tools" button and a "Small Notes >" button, for example... Figure 15The "Small Notes >" button (as shown in 1521) appears in the report summary section (as shown in 152). When an object clicks (or long-presses or performs other operations) a tool control, the system responds and displays tutorials for the relevant auxiliary tools. For example, if the object's browsing goal is "learn badminton serve," the system can provide user guides for auxiliary tools such as video editing tools, note-taking tools, or practice timers.

[0378] Among these features, video editing tools help users edit and save key teaching segments for easy repetition. Note-taking tools (such as mini-notes) allow users to quickly jot down key points while watching videos or reading articles for easy review. Practice timers allow users to set practice time to ensure they complete specific tasks within a specified time.

[0379] like Figure 16 As shown, this is a schematic diagram illustrating a tutorial on using small notes and a summary report in an embodiment of this application. Wherein, Figure 16 Interface 161 is the tutorial interface for using the Mini Notes tool. This interface provides a detailed introduction to how to use Mini Notes, and users can also view tutorials that are not yet fully displayed on the current interface by swiping. In addition, a shortcut to Mini Notes is also presented on this interface, such as 1611 in the lower right corner of interface 161. Users can create a new note with one click by clicking 1611.

[0380] Specifically, interface 161 can be displayed after the user triggers the first tool viewing operation based on the viewing control. The first tool viewing operation is to distinguish it from the second tool viewing operation. Both of these report viewing operations can be triggered based on the viewing control, but the specific triggering methods are different. For example, the first tool viewing operation is a click, and the second tool viewing operation is a long press; or the first tool viewing operation is a long press, and the second tool viewing operation is a double click, and so on.

[0381] In the above embodiments, by providing targeted auxiliary tools, users can complete learning tasks more efficiently and reduce time wasted due to unfamiliarity with the tools. Detailed user tutorials ensure users can quickly get started, improving the overall user experience. Furthermore, this application supports providing corresponding auxiliary tools based on different browsing goals, enhancing the system's personalized service capabilities.

[0382] Optionally, considering length and user experience, the report summary presented in the content browsing report interface may be a brief summary. The client may also respond to another tool viewing operation triggered by the user through a tool control, presenting detailed report summary information. This detailed report summary is generated through at least one of the following methods:

[0383] (1) Generated based on the notes of the object being used.

[0384] In other words, the report summary was generated based on notes taken by users while browsing the recommended content;

[0385] Specifically, the system collects and organizes notes taken by users while browsing recommended content, automatically generating detailed reports and summaries. This approach not only preserves the user's personalized understanding and key points but also provides structured review material.

[0386] (2) Automatically generated based on recommended content.

[0387] In other words, the report summary is automatically generated based on the user's browsing of relevant recommended content.

[0388] Specifically, based on the user's browsing of relevant recommended content, the system automatically extracts key knowledge points and important information, generating a detailed report summary. This approach ensures the comprehensiveness and accuracy of the summary, helping users quickly review and consolidate what they have learned.

[0389] Still with Figure 16 As shown in the example, Figure 16 Interface 162 in this embodiment is the report summary details interface, which displays more complete and detailed report summary content. Users can also view summaries that are not yet fully presented on the current interface through operations such as swiping. For example, in addition to... Figure 15 In addition to briefly summarizing the core points of badminton service, such as "stance" and "ready position," the book also introduces "swing technique," "swing motion," and "spinning the shuttlecock."

[0390] Specifically, interface 162 can be displayed after the user triggers the second tool viewing operation based on the viewing control. The second tool viewing operation is to be distinguished from the first tool viewing operation mentioned above. Both of these report viewing operations can be triggered based on the viewing control, but the specific triggering methods are different, which will not be repeated here.

[0391] The reports generated through any of the above methods provide a summary of details, allowing users to more easily review and understand previous learning or browsing content, further improving learning effectiveness and task completion rates.

[0392] In addition, to facilitate flexible switching and returning during browsing, another optional implementation is to add a target return control to the content browsing report. Using this control, users can easily return to the content recommendation interface and select other recommended content to continue browsing. One optional implementation is as follows:

[0393] The content browsing report in this application may also include a target return control, which allows the user to return to the content recommendation interface to continue browsing. Optionally, the client returns to the content recommendation interface in response to a return operation triggered by the user through the target return control.

[0394] The target return control can be displayed anywhere in the content browsing report, specifically in a prominent location, such as around the target completion progress bar. This article does not impose any specific restrictions on this.

[0395] like Figure 15 1512 in the figure refers to a target return control in this embodiment of the application, which is displayed below the target completion progress bar 1511. Specifically, the content browsing report interface provides a prominent target return control, such as a "Return to Recommended Content" button. When the user clicks the target return control, the system responds and immediately jumps back to the content recommendation interface, displaying recommended content related to the current browsing target. In this way, the user can decide whether to continue the current target or select new recommended content after viewing the content browsing report.

[0396] In the above implementation, by introducing a target return control, users can return to the recommended interface at any time to re-evaluate and select content, maintaining a high degree of flexibility. The seamless return mechanism ensures browsing continuity, preventing deviation from the target due to interruptions. Simultaneously, users can quickly switch and return as needed, optimizing time management and ensuring effective time utilization, thereby significantly improving task completion rates. This approach not only enhances the user experience but also effectively promotes goal achievement.

[0397] To further improve the effectiveness of users in achieving their browsing goals, this application adds a testing function. The system can accurately evaluate user behavior under different goals and push corresponding test results accordingly, thereby optimizing user experience and service quality. An optional implementation is as follows:

[0398] Based on the current goal completion rate, target detection content that matches the goal completion rate is dynamically presented. The goal completion rate is determined based on the user's interactive behavior while browsing recommended content.

[0399] In this embodiment, the system can determine the degree of goal completion by analyzing the user's interactive behavior (such as clicks, dwell time, note-taking, etc.). For example, when learning the "badminton serve," if the user watches a video tutorial and takes notes, the system will evaluate the degree of goal completion based on these behaviors. For specific evaluation methods, please refer to the relevant instructions on the server side, which will not be repeated here.

[0400] Optionally, the object detection content is used to evaluate and assist users in completing browsing goals, ensuring goal achievement and providing support, including but not limited to at least one of the following:

[0401] (1) Test content used to detect whether the object has completed the object browsing target.

[0402] The test content refers to a series of assessment tools and activities presented by the system when it detects that the user's goal completion rate has reached a certain percentage (e.g., 60%). These tests are used to verify whether the user has completed or mastered the set browsing goals. The test content helps users check their browsing results, consolidate their knowledge, and provide opportunities for practical application.

[0403] Optionally, the test content may include, but is not limited to, some or all of the following:

[0404] Online quizzes, mock exams, self-assessment questionnaires, case studies, interactive exercises, etc.

[0405] Online quizzes, in particular, are questions provided by the system in the form of multiple-choice, fill-in-the-blank, or short-answer questions, related to the user's learning objective, to help users assess their learning outcomes. For example, if a user's learning objective is "to learn the badminton serve," and the objective completion rate reaches 60%, the system can present an online quiz containing the following questions: Multiple-choice question: "What is the correct grip?" Fill-in-the-blank question: "The swing angle is usually maintained at ____ degrees." Short-answer question: "Please briefly describe the three main phases of the serve."

[0406] Simulated practice refers to providing simulated scenarios or virtual environments that allow users to actually operate and apply their learned knowledge in a near-realistic environment. For example, for the goal of "learning the badminton serve," the system can provide a virtual badminton court where users can practice serving. The system will provide feedback and scores based on the user's actions to help them improve their skills.

[0407] A self-assessment questionnaire guides users through a series of questions to assess their understanding of the target content. For example, the system might present a self-assessment questionnaire with questions such as, "Are you able to accurately describe the key points of your serve?" and "How confident are you in your mastery of serve techniques?"

[0408] Case analysis involves providing specific cases or scenarios and requiring users to apply their learned knowledge to analyze and solve them. For example, the system could display a badminton match video and ask users to analyze it and point out the strengths and weaknesses of the players' serves.

[0409] Interactive exercises refer to activities that help users deepen their memory and understanding of knowledge points through interactive exercises, such as drag-and-drop matching and sorting. For example, a system could design an interactive exercise that requires users to arrange the steps of a serve in the correct order, or to match different serving techniques with their application scenarios.

[0410] It should be noted that the test items listed above are just simple examples. Other test items are also applicable to the embodiments of this application, and will not be described in detail here.

[0411] Through these diverse test contents, the system not only helps users comprehensively test and consolidate their knowledge, but also provides opportunities for practical operation and application, ensuring that users truly master the key content of the browsing target.

[0412] (2) Other recommended content to support using objects to complete object browsing goals.

[0413] In this application embodiment, other recommended content refers to the additional resources and support provided by the system to users who have not fully achieved their goals, in order to help them better complete the set browsing goals.

[0414] Optional, other recommended content can be a simple list of recommended content, links, descriptions, etc., or it can be specific content, including but not limited to some or all of the following:

[0415] Advanced tutorials, supplementary materials, community discussions, practical exercise resources, personalized learning paths, etc.

[0416] Advanced tutorials refer to more in-depth tutorials or advanced techniques recommended by the system after it detects that the user has mastered the basic content, to help them further improve their skills. For example, if the user's browsing goal is "learn badminton serving techniques," after the user has mastered the basic serving skills, the system can recommend the following advanced tutorials: video tutorial: "Advanced Serving Techniques Analysis - How to Improve Serve Success Rate"; article: "The Secrets of Professional Players' Serving: From Basics to Mastery," etc.

[0417] Supplementary materials refer to additional learning resources, such as relevant literature, case studies, or expert lectures, to help users consolidate and expand their knowledge. For example, for the goal of "learning badminton serving techniques," the system can recommend the following supplementary materials: Literature: "Badminton Science: Analysis of Serving Techniques"; Case Studies: "Analysis of Classic Serving Cases in Top-Level Tournaments"; Expert Lectures: "Renowned Coaches Explain the Latest Trends in Badminton Serving Techniques," etc.

[0418] Community discussions refer to guiding users to join relevant forums or communities to exchange ideas and experiences with other learners, promoting interaction and mutual progress. For example, the system can recommend users to join the following community discussion platforms: Forums: "Badminton Enthusiasts Forum - Serving Skills Zone"; Social Media Groups: "Badminton Training Camp - Serving Skills Exchange Group"; Online Course Communities: "Badminton Online Courses - Student Interaction Zone", etc.

[0419] Practical training resources refer to opportunities that provide hands-on practice, such as simulated practice environments or offline activity information, to help users apply theoretical knowledge to practice. For example, the system can recommend users to participate in the following practical training resources: simulated practice: "Virtual badminton court - serve practice mode"; offline activities: "Local badminton club - weekly serve skills training class", etc.

[0420] Personalized learning paths refer to customized learning paths based on the user's progress and interests, providing targeted learning suggestions and plans. For example, the system can generate a personalized learning path based on the user's current progress: "Next learning suggestion: Master serve spin techniques and arrange a week-long specialized training plan," etc.

[0421] Through these diverse additional recommendations, the system not only helps users consolidate their existing knowledge but also provides opportunities to further enhance their skills, ensuring that users can efficiently achieve their browsing goals and gain a richer learning experience.

[0422] Optionally, different target assessment content can be presented for different target completion levels. For example, if a user sets the goal of "learning badminton serving techniques," in the initial stage (0%-40% completion): the system recommends basic tutorial videos and text-based explanations. The user watches the videos and takes notes, and the system assesses the target completion level as 30%. In the intermediate stage (40%-70% completion): the system presents an online quiz, allowing the user to test their mastery of basic serving techniques, and the system assesses the target completion level as 60%. In the later stage (70%-100% completion): the system recommends advanced tutorials and simulated practice, allowing the user to further solidify their skills through simulated practice, and the system assesses the target completion level as 90%. Finally, the user confirms goal achievement through a self-assessment questionnaire; and so on.

[0423] In the above implementation, by dynamically presenting test content that matches the target completion level, the system not only helps users understand their learning progress in a timely manner and consolidate what they have learned, but also significantly improves the target completion rate. Personalized recommended content provides the most suitable support and guidance based on each user's completion level, enhancing the user experience and engagement, and making the learning process more vivid and interesting. In addition, the system can flexibly adjust the recommended content based on the user's interactive behavior and test results, ensuring that the user always focuses on the most relevant and valuable information, optimizing the learning path. This approach comprehensively improves the user's goal achievement efficiency and overall satisfaction.

[0424] It should be noted that the above description mainly focuses on the content recommendation method in this application embodiment from the client side. The following description will focus on another content recommendation method in this application embodiment from the server side:

[0425] See Figure 17 The diagram shown is an implementation flowchart of another content recommendation method provided in this application embodiment. Taking the server as the execution subject as an example, the specific implementation flow of this method is as follows: S171~S172:

[0426] S171: After receiving a target setting request from the client, send a target prompt message to the client; wherein, the client is used to present the target prompt message in the target setting interface; the target setting interface is presented by the client in response to the first setting operation triggered by the current browsing interface, and the target setting interface includes: at least one preset browsing target; target prompt message, used to guide the user to set the browsing target by referring to at least one preset browsing target.

[0427] In this embodiment, after a user triggers a first setting operation on the current browsing interface, the user can invoke a broad target setting function. The client responds to the first setting operation and presents a target setting interface containing at least one preset browsing target. During this process, the preset browsing targets can be stored on the client side, or they can be requested from the server by the client each time the user triggers the first setting operation, and then sent to the client by the server.

[0428] In the target setting interface of this application, the user can further trigger a second setting operation to set the current browsing target (i.e., the object's browsing target). During the process of setting the browsing target, the client can send the target currently entered by the user (which can be empty) to the server. The server judges and analyzes in real time whether the target currently entered by the user is a clear target or a vague target, and based on the judgment result, pushes relevant and different target prompt information to the user in real time, such as the first target prompt information and the second target prompt information in this application. The specific content of the target prompt information can be found in the relevant description on the client side mentioned above, and will not be repeated here.

[0429] An optional implementation is to carry out S171 according to the following process, including the following steps S1711 to S1713 ( Figure 17 (Not shown):

[0430] S1711: Obtain the initial browsing target included in the target setting request; the initial browsing target is set using the object based on the target setting interface.

[0431] Specifically, the initial browsing target is the first target entered by the user during the browsing target input process. In practical applications, the user can input the initial browsing target through voice, text, or even by directly selecting a preset browsing target. Regardless of the input method, the final result is the text of the initial browsing target entered by the user, which is then used for subsequent processing.

[0432] S1712: Detect whether the initial browsing target is clear.

[0433] Optionally, during the process of using an object to input the browsing target, the server utilizes NLP techniques and machine learning algorithms to determine whether the browsing target currently input by the object (i.e., the initial browsing target) is explicit. The following are the specific steps and methods for the server-side to determine the explicitness of the target:

[0434] First, the server performs data preprocessing on the initial browsing target to ensure the effectiveness and accuracy of subsequent analysis. In this embodiment, the data preprocessing of the initial browsing target includes, but is not limited to, the following preprocessing methods:

[0435] Text cleaning, word segmentation, normalization and standardization, semantic enhancement, deduplication and merging.

[0436] Text cleaning removes irrelevant characters, punctuation, and stop words from the initial browsing target, simplifying the text content and ensuring the effectiveness and accuracy of subsequent analysis. Word segmentation breaks down the initial browsing target into words or phrases, facilitating further feature extraction and semantic analysis. Regularization and standardization unify text format and simplify vocabulary, ensuring that different variations of the same word are recognized as the same word. Semantic enhancement improves the server's understanding of the text content, ensuring a more accurate capture of the user's intent. Deduplication and merging reduce redundant information and improve text conciseness.

[0437] It should be noted that the preprocessing methods listed above are only simple processing methods. Other preprocessing methods are also applicable to the embodiments of this application, and will not be described in detail here.

[0438] Next, the server uses feature extraction to identify and quantify key information about the initial browsing target.

[0439] In this embodiment, keywords can be extracted using algorithms such as Term Frequency-Inverse Document Frequency (TF-IDF) and TextRank to identify the most representative words, providing a foundation for subsequent analysis. Then, keywords can be converted into vector representations using methods such as Word2Vec or Global Vectors for Word Representation (GloVe) to capture the semantic relationships between words and enhance the understanding of the user's intent.

[0440] It should be noted that the keyword extraction algorithms listed above are only simplified examples. Other preprocessing methods are also applicable to the embodiments of this application, such as YAKE (Fast Automatic Keyword Extraction) and KP-Miner (Keyword Miner), which will not be elaborated here. Similarly, the models for converting keywords into vector representations listed above are also simple examples. In addition, other vector representation models are also applicable to the embodiments of this application, such as the FastText model, Bidirectional Encoder Representations from Transformers (BERT), Universal Sentence Encoder (USE), Transformers and their variants (such as the Robustly Optimized BERT Approach (RoBERTa)), which will not be elaborated here.

[0441] Finally, the server assesses whether the target of the initial browsing is clear and specific by judging the target's clarity. This stage includes two parallel schemes: fuzzy target recognition and explicit target recognition.

[0442] For fuzzy target recognition, the server can identify ambiguous expressions by analyzing lexical diversity and semantic ambiguity. Specifically:

[0443] Regarding lexical diversity, if the initial browsing target contains a large number of irrelevant or repetitive words, it may indicate that the target is unclear. The server can assess the clarity of the initial browsing target by detecting the distribution of these words. Specifically, it can determine whether the initial browsing target is too broad or vague by counting word frequencies, removing stop words, and analyzing the concentration of the remaining words.

[0444] To address semantic ambiguity, servers can utilize pre-trained language models (such as BERT, RoBERTa, etc.) to analyze the semantics of the initial browsing target and identify whether there are ambiguous or polysemous expressions. This helps to discover potential ambiguities and uncertainties, ensuring that the system can accurately understand the user's intent and provide more precise content recommendations.

[0445] By using one or more of these two methods, the server can effectively determine whether the initial browsing target is too broad or vague.

[0446] For explicit target identification, through topic consistency and intent recognition, the server can confirm a clear target category, ensuring that the input from the user is focused on a specific domain or task. Specifically:

[0447] For topic consistency, the server analyzes the initial browsing goal using a topic model (such as Latent Dirichlet Allocation (LDA)) to determine if a clear topic or intent exists. This step ensures that the user's input is focused on a specific domain or task, thereby improving the relevance and accuracy of content recommendations.

[0448] For intent recognition, the server can train a classifier (such as a support vector machine or random forest) to identify common, explicit target categories (such as learning new knowledge, finding inspiration, entertainment, and relaxation) and determine whether the initial browsing goal belongs to these categories. This step helps the server accurately understand the actual needs of the user and provide more personalized services and content recommendations.

[0449] By using one or more of these two methods, the server can identify specific target categories, ensuring that the initial browsing goals using object input are focused on a specific area or task, thereby improving the overall user experience and goal achievement rate.

[0450] It should be noted that the methods listed above for detecting whether the initial browsing target is clear are just simple examples. Other detection methods are also applicable to the embodiments of this application, and will not be described in detail here.

[0451] After completing the above analysis, the server can interact with the user through a real-time feedback mechanism to help them further clarify their goals or confirm their current input. This mechanism not only improves the user experience but also ensures the high relevance and targeting of the recommended content. The specific implementation method is as follows: S1713:

[0452] S1713: If the initial browsing target is determined to be unclear, a guiding question is generated based on the initial browsing target, and the guiding question is sent to the client through target prompt information; wherein, the guiding question is used to guide the user to update the initial browsing target to the object's browsing target.

[0453] Specifically, if the server determines that the user's goal is vague, i.e., the initial browsing objective is unclear, it will provide real-time feedback to the client with target-specific prompts. These prompts, which may take the form of guiding questions or other methods, suggest that the user further refine their objective. Examples include: "Which area of ​​knowledge are you looking for?", "Could you describe specifically the area where you are seeking inspiration?", and "How long do you want to relax and have fun?" These prompts aim to guide the user to provide more details, gradually refining their browsing objective and helping the server more accurately understand the user's actual needs.

[0454] Optionally, the client-side can present guiding questions to the user through a dialogue in the goal setting interface, as detailed in the embodiments above. Correspondingly, the server-side can utilize a dialogue management server (such as Rasa) to conduct multi-turn dialogues with the user, gradually guiding them to clarify their goals. Through natural dialogue, the server can gain a deeper understanding of the user's intentions and provide personalized suggestions and support.

[0455] During this process, the server can also continuously optimize the aforementioned judgment model or mechanism for target clarity based on feedback and adjustments from users, thereby improving accuracy. Specifically, each additional information provided by users can be used to improve the model, such as fine-tuning the parameters of the topic model based on user feedback, optimizing the accuracy of classification and topic detection, and ensuring that future judgments are more accurate and personalized.

[0456] Optionally, if the server determines that the user's goal is clear, it will confirm and continue the subsequent process. For example, "The system is matching you with customized content." Through this instant confirmation, the user can confidently continue browsing, knowing that the system understands their needs.

[0457] In addition, if the initial browsing goal is clear when using object input, the server can also provide target hints to the client, such as "the target should conform to smart rules as much as possible".

[0458] In summary, through these real-time feedback and interactive optimization measures, the server can not only promptly help users clarify their goals but also continuously improve its intelligence, providing a superior service experience. This approach ensures high relevance of recommended content and user satisfaction, enabling users to achieve their browsing goals more efficiently.

[0459] Of course, these target prompts can also be stored on the client side, or analyzed and generated by the client side, and then presented directly to the user by the client, etc. The specific implementation methods are as described above, and will not be repeated here.

[0460] In this embodiment of the application, after the final input object browsing target is determined using the object, the server can perform the following operations:

[0461] S172: After setting the browsing target of an object using the object, search for recommended content related to the browsing target of the object and send the search results to the client; wherein, the client is used to display the recommended content through the content recommendation interface.

[0462] In this step, the server can combine one or more factors such as the user's historical behavior, current time, popular trends, and the user's immediate feedback (such as immediate interactive behavior, emotional reactions, etc.) and use one or more technologies such as advanced NLP, sentiment analysis, and multimodal content analysis to search for suitable recommended content. This provides users with diverse, personalized, and inspiring content recommendations, and sends the search results to the client for display through the content recommendation interface.

[0463] To more accurately meet the broad target needs of users, an optional implementation method is to carry out S172 according to the following process, including the following steps S1721 to S1722 ( Figure 17 (Not shown):

[0464] S1721: Based on the historical characteristics of the user and the browsing target of the user, search for the first recommended content related to the browsing target of the user, and send the first recommended content to the client.

[0465] Historical characteristic information refers to data collected and stored by the system regarding the past behavior and preferences of users. This information includes, but is not limited to, at least one of the following:

[0466] Browsing history: Websites, pages, and applications visited and the time spent there; Search history: Search keywords and queries used; Historical behavior: Interactions such as clicks, likes, comments, shares, and favorites made by the user during browsing history; Interest tags: Interest areas and preference tags derived from long-term behavioral analysis, such as technology, music, and history; Purchase history: For e-commerce platforms, this also includes past purchase behavior and product reviews.

[0467] By utilizing this historical feature information, the system can construct historical feature information of an object, predict its future behavioral patterns and interests, and thus provide more personalized recommendations and services. Therefore, in this embodiment, S1712 belongs to the preliminary search stage, used to perform preliminary and simple search recommendations to initially present recommended content to the user.

[0468] S1722: Based on the user's interaction characteristics with the currently recommended content and the user's browsing goal, search for second recommended content related to the user's browsing goal and send the second recommended content to the client.

[0469] Interaction feature information refers to the specific reactions and behavioral data of users to recommended content in the current or recent period, including but not limited to at least one of the following:

[0470] User interaction with currently recommended content includes: real-time actions such as clicking, liking, commenting, sharing, and saving; user emotional responses to currently recommended content, such as emotions like pleasure, confusion, and dissatisfaction captured through natural language processing and sentiment analysis; user dwell time on currently recommended content, reflecting the content's attractiveness; user feedback ratings of currently recommended content, directly reflecting satisfaction; and revisit behavior, indicating whether users return to view the same currently recommended content multiple times, demonstrating the content's lasting appeal.

[0471] The currently recommended content here refers to the content that the system has recommended to the user after the user sets the target of the current object browsing.

[0472] In this embodiment, interactive feature information can help the system adjust its recommendation strategy in real time, ensuring that recommended content better matches the immediate needs and preferences of users, thereby improving user experience and engagement. Therefore, compared to S1711, S1712 belongs to the refinement search stage. In the refinement search stage, real-time feedback data from users can be used to dynamically adjust recommended content, supporting users in dynamically refreshing recommended content.

[0473] In addition, in S1711 or S1712, you can also combine the current time, popular trends, etc. to search.

[0474] The content recommendation methods described in S1721-S1722 above are suitable for fuzzy targets. In the process of S1722, the original fuzzy target can be continuously refined, thereby recommending more accurate search results to the user. For specific targets, only a preliminary search can be performed. Of course, the same search method as for fuzzy targets can also be used, which will not be elaborated here.

[0475] In the above implementation, through the gradual optimization of these two stages, the system can not only provide relevant content during the initial recommendation, but also continuously improve the recommendation quality based on the user's real-time feedback, ensuring that the recommended content is more in line with the user's actual needs and changing interests, thereby improving the overall user experience.

[0476] Taking the example of using different methods to search for specific or vague targets:

[0477] In another alternative implementation, the server matches relevant customized content (i.e., recommended content related to the user's browsing goals) based on the explicit or implicit goals specified by the user. Wherein:

[0478] For a specific goal, the recommendation algorithm for matching recommended content relies on the specific goal input by the user, such as "learn badminton serve for 1 hour and understand the key points of serve". Natural language processing technology and machine learning algorithms are used to analyze these goals and make content recommendations accordingly. The server will use algorithms such as collaborative filtering and content recommendation to find content that is highly relevant to the user's specific browsing goal and make recommendations based on the specific goal set by the user.

[0479] For fuzzy goals, when a user inputs a vague goal, such as "to relax and have some fun," "to learn new things," or "to find inspiration," the server collects and analyzes the user's contextual information (such as current time, geographical location, device status, etc.) in real time based on the input goal. It then combines this information with the user's behavioral preferences, interest tags, and big data analytics and machine learning algorithms to filter out relevant and high-value content from massive amounts of online information for recommendation. In this process, through context-aware recommendation and dynamic interest modeling, the server can more accurately understand the user's needs and provide personalized recommendation services.

[0480] Specifically, for fuzzy targets, the server not only relies on the content input by the user but also considers the following factors: Contextual information: including but not limited to current time, geographical location, and device status, to better understand the user's immediate needs. Behavioral preferences: identifying the user's long-term interests and preferences based on their browsing history, click behavior, and interaction patterns. Interest tags: further refining and clarifying the user's specific needs using past behavioral tags. Big data analytics and machine learning algorithms: predicting and recommending content that best matches the user's current needs by analyzing a large amount of behavioral data from similar users. Through these comprehensive measures, the server can significantly improve the relevance and accuracy of recommended content, ensuring a highly personalized and valuable browsing experience for the user. The following are the technical principles behind this process:

[0481] First, the user's behavioral preferences and interest tags are analyzed, which is divided into two parts: data collection and tag extraction.

[0482] Data collection refers to the server first collecting users' historical browsing data, click behavior, dwell time, etc., to build a user behavior profile. Whether it's for entertainment and relaxation, finding inspiration, or learning new knowledge, key information can be extracted from users' browsing behavior of different types of content.

[0483] Tag extraction refers to extracting user interest tags based on user behavior data (which may include historical behavior data, real-time interaction data, etc.) using natural language processing techniques. For goals related to "entertainment and relaxation," tags can be extracted, such as favorite movie genres, music styles, or knowledge areas. For goals related to learning new knowledge, tags can be extracted based on the subject areas the user has previously browsed.

[0484] Next, the ambiguous target set by the user object will be parsed, specifically divided into two parts: semantic understanding and intent refinement.

[0485] Semantic understanding refers to the server using a pre-trained language model to deeply understand the meaning of ambiguous goals such as "relax and have fun," "learn new knowledge," and "find inspiration," and identify potential points of interest. For "learn new knowledge," it can identify potentially interesting knowledge areas and learning methods. For "relax and have fun," it can identify potentially interesting video, music, or game categories.

[0486] Intent refinement refers to the server further refining the user's intent by combining the user's historical behavior and tags, and inferring the specific form of entertainment or learning content the user may expect.

[0487] Next, we will perform context-aware recommendation and dynamic interest modeling, which will be divided into two parts: context-aware recommendation and dynamic interest modeling.

[0488] Context-aware recommendation refers to the server collecting and analyzing contextual information about the user in real time, such as current time, geographical location, and device status, based on the user's input goal. This makes the recommended content more relevant to the user's current contextual needs. For example, if a user inputs "relax and have fun" on a weekend evening, the server might recommend a lighthearted movie or concert. If the user inputs "learn something new" on a weekend evening, the server might recommend lighthearted science videos or online lectures; and during a weekday lunch break, it might recommend short knowledge cards, etc.

[0489] Dynamic interest modeling combines multiple algorithms such as collaborative filtering, content recommendation, and knowledge-based recommendation, using online learning algorithms (e.g., Follow-The-Regularized-Leader (FTRL) models) to update the weights of each recommendation algorithm in real time. Through multi-dimensional evaluation (timeliness, authority, user feedback, etc.), it ensures that recommended content is not only relevant to the user's goals but also of high quality and value. For the goal of learning new knowledge, greater emphasis is placed on the authority and depth of knowledge in the content.

[0490] Finally, high-value content is selected, which consists of two parts: content quality assessment and personalized ranking.

[0491] Content quality assessment refers to the server using machine learning algorithms to evaluate the quality of massive amounts of content and filter out high-quality, valuable content. For example, for the goal of learning new knowledge, the focus is on evaluating the accuracy and richness of the knowledge in the content.

[0492] Personalized ranking refers to combining user-specific characteristics such as interest tags and behavioral preferences to personalize the recommended content, ensuring that users first see content that best suits their needs. For example, when learning new knowledge, content related to and expands upon the user's existing knowledge base is prioritized.

[0493] It should be noted that the technical implementation principles listed above can effectively obtain the historical and interactive feature information of an object. When searching for recommended content for fuzzy targets, the system can gradually recommend more accurate and relevant content to the user through two stages: preliminary search and refined search.

[0494] Optionally, the expected browsing time for the second recommended content is determined based on interaction feature information and the browsing goals of the target audience.

[0495] The expected browsing time refers to the ideal browsing time for the user on the second recommended content, predicted based on the user's interaction characteristics and browsing goals, which is used to optimize the accuracy of content recommendations.

[0496] In this embodiment, the server can intelligently allocate browsing time based on the user's browsing goals and interaction data, thereby preventing the user from becoming overly engrossed in low-value content.

[0497] The following are simple examples using two vague goals: "to relax and have some fun" and "to learn new knowledge":

[0498] For entertainment and relaxation purposes, the server will prioritize allocating time to high-value entertainment content (such as high-quality videos and fun games) based on the user's entertainment and relaxation goals. The specific allocation ratio can be adjusted based on the user's interaction behavior and satisfaction evaluation results.

[0499] For example, if a user primarily watches short videos and achieves high satisfaction in the past hour, the server may continue to recommend similar short video content for a period of time in the future, but will limit the duration of each viewing session to prevent excessive addiction.

[0500] When learning new knowledge, the server will prioritize allocating time to high-value learning content (such as educational videos, professional articles, etc.) and adjust the time allocation based on the user's learning progress and feedback.

[0501] For example, if a user is reading a professional article and shows high interest and engagement, the server may extend the article's recommendation time, but will insert short break prompts at appropriate times to prevent fatigue.

[0502] In addition, the server monitors the user's interactive behavior and emotional state in real time, performing real-time target compliance checks to dynamically adjust time allocation. For example, if a user frequently likes and comments on entertainment content within a certain period, the server will appropriately increase the recommendation time for that type of content; conversely, if a user frequently switches content or shows signs of boredom within a certain period, the server will reduce the recommendation time for entertainment content and increase the recommendation of learning or other high-value content.

[0503] Through the above implementation methods, the problem of time usage can be effectively managed and optimized by intelligently allocating browsing time when browsing information streams.

[0504] To prevent users from becoming overly engrossed in low-value content, in addition to the goal-oriented time allocation mentioned above, the server can also perform real-time goal compliance detection and notify the client to issue a reminder or warning when the user approaches a certain time limit. One possible implementation is as follows:

[0505] After detecting that the user has been continuously browsing secondary content for a preset period of time, a browsing prompt message is sent to the client. The browsing prompt message is used to indicate that the secondary content is not related to the user's browsing target. The secondary content is content whose relevance to the user's browsing target is lower than a preset relevance threshold.

[0506] In other words, when a user spends an extended period on low-value content, the server will issue a reminder or warning to guide the user to adjust their browsing strategy. The specific method is as follows: The server uses content analysis and user behavior data to identify low-value content (such as videos with high repetition and low interactivity). If a user watches multiple low-value short videos consecutively, the server will notify the client to issue a warning when the preset time limit (e.g., a preset duration of 10 minutes) is approached, prompting the user to adjust their browsing strategy through browsing prompts. For detailed implementation instructions, please refer to the relevant documentation on the client side; these will not be repeated here.

[0507] In addition, the server can set a browsing time limit related to the user's browsing goals and behavior data. Specifically, this can refer to the maximum browsing time within a preset time interval (such as daily or weekly) (or it can be set by the user). During browsing, when the user approaches or reaches the set browsing time limit, the server can also issue a reminder or warning through a time limit prompt message to guide the user to adjust their browsing strategy.

[0508] For example, for the goal of entertainment and relaxation, a maximum of 2 hours of video viewing per day might be set; for the goal of learning new knowledge, a maximum of 4 hours of study time per day might be set; and so on.

[0509] In the above implementation, by promptly reminding users to keep their focus on the main goal and avoid wasting time on irrelevant content, this approach not only improves time utilization but also helps users stay focused and avoid excessive immersion in irrelevant content, thereby improving concentration and time management efficiency.

[0510] Optionally, this application supports dynamically presenting target detection content matching the current target completion level on the client side. Correspondingly, on the server side, it is necessary to determine the target completion level and generate matching target detection content. One optional implementation is as follows:

[0511] The server detects the user's interaction with the currently recommended content and determines the matching score between the interaction and the user's browsing goal; it compares the matching score with a preset completion threshold to obtain the current goal completion level; based on the goal completion level, it sends the corresponding target detection content to the client.

[0512] The target detection content includes at least one of the following: test content used to detect whether the user has completed the object browsing target; and other recommended content used to support the user in completing the object browsing target. For details regarding the target detection content, please refer to the relevant descriptions on the client side mentioned above, which will not be repeated here.

[0513] In simple terms, the process by which the server detects the user's interaction with the currently recommended content and determines the matching score between the interaction and the user's browsing target is the target matching detection process. Specifically, the higher the matching score, the higher the target matching, and vice versa.

[0514] Based on the obtained matching score, the current target completion level of the target object can be determined by comparing the matching score with the preset completion threshold.

[0515] The logic of target conformity detection and target completion assessment and feedback is explained in detail below:

[0516] During the browsing of recommended content and the search for new recommended content, the server can perform target conformity detection. Specifically, the server detects the user's interactive behavior and analyzes the degree of understanding of the user's behavior, thereby matching the user's behavior with the target.

[0517] Taking the vague goals of "relaxing and having fun" and "learning new knowledge" as examples, in order to accurately detect the user's interactive behavior and analyze their understanding of the goals, the server employs a series of specific technical implementation logics and details to ensure efficient matching of the user's behavior with the set goals. The following is a detailed process description:

[0518] First, the server performs data collection and preprocessing, which is the foundation of the entire process. The server collects user interaction data from multiple dimensions on the platform and preprocesses it for subsequent analysis. Below are a few examples of these dimensions:

[0519] (1) Behavioral data.

[0520] The server collects various interactive behaviors of users on the platform, including but not limited to clicks, viewing time, likes, comments, shares, and favorites. This behavioral data forms the basis of the user's behavioral sequence. Through clustering algorithms, the server can extract the user's main interests from these behaviors. Whether choosing entertainment and relaxation or learning new knowledge, user preferences can be analyzed from these behaviors.

[0521] For example, for the goal of entertainment and relaxation, longer viewing time and frequent likes may indicate that the user is enjoying the content; while for the goal of learning new knowledge, saving and taking notes reflect the user's interest and engagement in the learning content.

[0522] (2) Sentiment data.

[0523] The server utilizes NLP and facial expression recognition technology to deeply analyze the user's emotional tendencies and states (such as pleasure and focus). For example, by combining facial expressions captured by a camera (such as those captured by the front-facing camera of an available terminal device), the server can comprehensively determine the user's emotional state, ensuring that recommended content can effectively improve the user's focus or bring a pleasant experience.

[0524] For example, for the goal of entertainment and relaxation, the server focuses on capturing the user's feelings of pleasure and relaxation to ensure that the recommended content truly makes the user feel relaxed and happy. However, for the goal of learning new knowledge, more attention is paid to the user's focus and confusion during the learning process, using sentiment analysis to help adjust recommended content and improve learning effectiveness.

[0525] (3) Tag data.

[0526] Based on the behavioral sequences of the users, the server further utilizes clustering algorithms to extract key points of interest, and combines these with the users' historical labels and preferences (also known as behavioral preferences) to refine the classification of points of interest.

[0527] For example, for the goal of entertainment and relaxation, the server can categorize content based on the user's past favorite entertainment types (such as comedy, music, games, etc.) to ensure that recommended content is more aligned with the user's preferences. Conversely, for the goal of learning new knowledge, the server can categorize content based on the user's past learning domain tags (such as programming, history, art, etc.) to provide more targeted learning resources.

[0528] Through these methods, the server can not only better understand the current needs of users but also predict future changes in interests, providing more personalized services. These meticulous data collection and preprocessing steps allow the server to comprehensively and accurately capture the behavioral characteristics and emotional state of users, laying a solid foundation for subsequent target matching and personalized recommendations. This not only enhances the server's intelligence but also ensures high relevance of recommended content and optimized user experience.

[0529] Based on data collection and preprocessing, the server can use feature engineering to process the aforementioned data types and convert them into numerical forms that can be used for analysis. This feature data not only helps in detecting target completion but also provides personalized support content.

[0530] (1) Behavioral characteristics.

[0531] Based on the aforementioned behavioral data, the server converts various interactive behaviors of the user into numerical characteristics, such as the number of clicks, viewing time, likes, comments, shares, and favorites. These behavioral characteristics are of significant reference value for both entertainment and relaxation, as well as learning new knowledge.

[0532] For example, longer viewing times and frequent likes may indicate that users are enjoying entertainment content, while saves and notes reflect users' interest and engagement with learning content.

[0533] (2) Emotional characteristics.

[0534] Corresponding to the above sentiment data, the sentiment analysis results are also quantified into numerical features, such as the degree of pleasure and the level of focus.

[0535] For example, when it comes to the goal of learning new knowledge, focus is particularly important because it directly reflects the user's level of engagement in the learning process.

[0536] (3) Contextual features.

[0537] In addition to the tag data, the server also considers factors such as time and scenario to extract relevant contextual features.

[0538] For example, when recommending new learning content in the evening, the system might favor lighthearted science articles or videos to cater to the needs of users at different times of day. Similarly, based on geographic location and device status, the server can further optimize recommended content to ensure it matches the user's current context.

[0539] Through these meticulous feature engineering processes, the server can comprehensively capture the user's behavioral patterns, emotional state, and contextual information, thereby more accurately assessing goal completion and providing personalized support content to ensure that the user successfully completes their set goals.

[0540] Based on the extracted features, the server constructs a dataset using the feature data (such as behavioral features, emotional features, and contextual features). Then, using the dataset, the server refines the target and updates the recommended content through stages such as machine learning, deep learning, and reinforcement learning.

[0541] The following section will first introduce the machine learning phase:

[0542] In this stage, the server uses a machine learning classification model to determine the user's potential target direction (such as entertainment and relaxation or learning new knowledge), thus performing an initial screening. The specific implementation method for this stage is as follows:

[0543] First, choose a suitable machine learning algorithm, such as random forest or support vector machine. These algorithms perform well in handling classification problems and can effectively cope with high-dimensional feature spaces.

[0544] Next, the dataset is divided into training and test sets. The classification model is trained using the training set, and the model performance is evaluated through methods such as cross-validation to ensure its generalization ability.

[0545] Next, the trained classification model is used to perform preliminary screening of the user's behavior and determine its possible target direction.

[0546] For example, the vague goal of "relaxing and having fun" can be broken down into several possible sub-goals, such as watching videos, listening to music, or playing games; the vague goal of "learning new knowledge" can be broken down into several possible sub-goals, such as learning scientific and technological knowledge, learning history and culture, or learning life skills.

[0547] During the initial screening process, by combining characteristic data such as the user's interests and sentiment analysis results, the sub-goals most likely to meet the user's needs are initially identified, thereby more accurately understanding their needs and providing personalized services or content recommendations.

[0548] The following describes the stages of deep learning:

[0549] After the initial screening, the server can use a deep learning model for fine-tuning to further optimize the matching scores. The specific implementation method for this stage is as follows:

[0550] After initial screening, the first step is to select a suitable deep learning architecture, such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN), which can capture the temporal and contextual information of behavior.

[0551] Next, the object behavior data will be converted into an input format suitable for deep learning models (such as time series data) to build a deep learning model, taking into account the temporal and contextual information of the behavior (if it has been processed in the previous preprocessing and feature engineering stages, this part does not need to be repeated; if the preprocessing and feature engineering stages were ignored, this part can be reprocessed). Then, the model will be trained using a large amount of data, and its performance will be improved through hyperparameter tuning and other methods.

[0552] Next, deep learning models are used to fine-tune the initial screening results, further optimizing the matching scores. Based on the refined sub-goals, the server uses multimodal fusion technology to sift through massive amounts of content to select relevant candidate sets (i.e., recommended content). For example, for the goal of learning new knowledge, learning resources from different fields can be selected. Furthermore, quality assessment and personalized ranking of the candidate sets ensure that the recommended content not only matches the user's fuzzy / precise goals but also possesses high value.

[0553] The reinforcement learning phase will be introduced first below:

[0554] After fine-tuning, to continuously optimize recommendation performance, the server can treat object behavior and target matching as a reinforcement learning problem, dynamically optimizing the matching score. The specific implementation method for this stage is as follows:

[0555] First, the server defines states, actions, and rewards, incorporating the behavior and feedback of the users into the environment model to build a reinforcement learning framework.

[0556] Based on this, the server selects a suitable reinforcement learning algorithm (such as policy gradient) and trains the agent to choose the optimal action in a given state through interaction with the environment, thereby maximizing the cumulative reward.

[0557] Next, dynamic optimization is performed. Specifically, in practical applications, the agent continuously adjusts its strategy based on user feedback, dynamically optimizing the matching score. The server displays recommended content to the user and collects their interactive feedback in real time. For example, for the goal of learning new knowledge, special attention is paid to the user's feedback on learning resources; by analyzing the user's feedback data, the matching strategy is adjusted and optimized to improve the accuracy of the next recommendation.

[0558] Through this series of steps, the server can comprehensively capture the user's behavioral patterns, emotional state, and contextual information, thereby more accurately assessing the match between content and goals and providing highly personalized and valuable recommendation services. Each stage is closely linked and progressively deepens, ensuring that the final recommended content not only matches the user's vague or precise goals but also possesses high value.

[0559] Building upon the above, to accurately assess the user's goal completion status and provide timely feedback, the server employs a comprehensive method to calculate the matching score and judges goal completion based on preset thresholds. Finally, a personalized feedback mechanism further optimizes the user experience. The specific implementation is as follows:

[0560] First, the server comprehensively utilizes the results of machine learning, deep learning, and reinforcement learning as listed above to calculate a matching score between the user's behavior and the set target. This score reflects the degree to which the user's current behavior matches the target requirements, providing a quantitative basis for subsequent completion assessments. The calculation time for the matching score can be flexibly set according to needs; it can be performed in batches at fixed time intervals (such as every ten minutes, every half hour, etc.) or updated dynamically in real time to ensure the timeliness and accuracy of the matching score. This article does not impose specific limitations on this, to adapt to the needs of different application scenarios.

[0561] Optionally, interactive behaviors include common behaviors and specific behaviors; common behaviors refer to interactive behaviors that are relevant to all types of browsing targets, while specific behaviors refer to interactive behaviors unique to a particular type of browsing target. Therefore, when calculating the matching score, an optional implementation method is as follows:

[0562] The first matching score is obtained by weighting and summing the common behaviors of the object within the preset statistical time period with the corresponding first score. Then, the second matching score is obtained by weighting and summing the specific behaviors of the object within the preset statistical time period with the corresponding second score. Finally, the sum of the first matching score and the second matching score is used as the current matching score.

[0563] In the embodiments of this application, different types of goals share common behaviors, but do not necessarily exhibit specific behaviors. Taking the goals of entertainment and relaxation and learning new knowledge as examples, the interactive behaviors under these two goals are as follows:

[0564] Common behaviors include, but are not limited to: clicking, viewing time, liking, commenting, saving, and sharing. Specific behaviors for learning new knowledge include, but are not limited to: private chat interaction, cross-application notes, screenshots, copying, and pasting. Entertainment and relaxation do not involve specific behaviors. When no specific behavior exists, the second score or its corresponding weight can be considered as 0.

[0565] Optionally, different interactive behaviors may correspond to different scoring mechanisms. For different common behaviors, the first score can be the same or different. The weight used when weighting the first score of common behaviors can be denoted as the first weight. For different common behaviors, the first weight can be the same or different. Similarly, for different specific behaviors, the second score can be the same or different. The weight used when weighting the second score of specific behaviors can be denoted as the second weight. For different specific behaviors, the second weight can be the same or different.

[0566] It should be noted that the first score, second score, first weight, second weight, etc., mentioned in this article can be flexibly set according to the target audience and actual scenario, etc., and will not be elaborated on here. A simple example is given below:

[0567] For example, for common behaviors, the scoring mechanism is as follows:

[0568] Click behavior: 1 point for each click, weight 0.1; Viewing time: 0.5 points for each minute of viewing, weight 0.2; Like behavior: 2 points for each like, weight 0.1; Comment behavior: 1 point for each comment, weight 0.2; Save behavior: 5 points for each save, weight 0.2; Share behavior: 5 points for each share, weight 0.2.

[0569] Optionally, the sum of the first weights is 1.

[0570] For a specific behavior, taking learning new knowledge as an example, the scoring mechanism is as follows:

[0571] Private chat interaction: 15 points for each private chat, weighted at 0.3; Cross-application notes: 20 points for each cross-application note, weighted at 0.3; Screenshot: 10 points for each screenshot, weighted at 0.2; Copy: 8 points for each copy, weighted at 0.1; Paste: 8 points for each paste, weighted at 0.1.

[0572] Optionally, the sum of the individual second weights is 1.

[0573] In summary, the matching score is calculated using the following formula for the goal of entertainment and relaxation:

[0574] S 娱乐 =w C ×dot C ×C+w T ×dot T ×T+w L ×dot L ×L+w Com ×dot Com ×Com+w Col ×

[0575] dot Col ×Col+w Sh ×dot Sh ×Sh.

[0576] Among them, S 娱乐 This indicates the matching score under the goal of entertainment and relaxation; w 点击 This is the first weight (e.g., 0.1) corresponding to the click action, dot C C represents the initial score (e.g., 1 point) for each click, and C represents the number of clicks; w T This is the first weight (e.g., 0.2) corresponding to the viewing behavior, dot T This represents the first score per minute of viewing, where T is the viewing duration (in minutes); w L This is the first weight (e.g., 0.2) corresponding to the "like" action, dot. L It is the first score corresponding to each like, where L is the number of likes; w Com This is the first weight (e.g., 0.1) corresponding to the comment behavior, dot Com It represents the first score for each comment; Com is the number of comments; w Col This is the first weight (e.g., 0.2) corresponding to the collection behavior, dot. Col This is the first score corresponding to each collection; Col is the number of collections. Sh This is the first weight (e.g., 0.2) corresponding to the collection behavior, dot Sh Sh represents the first score corresponding to each collection, and Sh represents the number of shares. In this formula, the specific number of shares refers to the number of shares counted within the calculation time of the matching score.

[0577] Where, the first matching score = S 娱乐 The second matching score is 0.

[0578] For the learning of new knowledge objectives, the matching score is calculated using the following formula:

[0579] S 学习 =w C ×dot C ×C+w T ×dot T ×T+w L ×dot L ×L+w Com ×dot Com ×Com+w Col ×

[0580] dot Col ×Col+w Sh ×dot Sh ×Sh+w PM ×dot PM ×PM+wAN ×dot AN ×AN+w SC ×dot SC ×SC+w CP ×dot CP ×CP+w PA ×dot PA ×PA.

[0581] Wherein, the first matching score = w C ×dot C ×C+w T ×dot T ×T+w L ×dot L ×L+w Com ×dot Com ×Com+w Col ×dot Col ×Col+w Sh ×dot Sh ×Sh, Second matching score = w PM ×dot PM ×PM+w AN ×dot AN ×AN+w SC ×dot SC ×SC+w CP ×dot CP ×CP+w PA ×dot PA ×PA.

[0582] Among them, S 学习 This represents the matching score under the learning new knowledge objective. The meanings of the weights and scores in the second matching score are explained above and will not be repeated here. PM This is the second weight (e.g., 0.3) corresponding to the "like" action, dot. PM It's the second score corresponding to each like; PM is the number of private chat interactions. AN This is the second weight (e.g., 0.3) corresponding to the "like" action, dot. AN It is the second score corresponding to each like, and AN is the number of times notes were taken across applications; w SC This is the second weight (e.g., 0.2) corresponding to the "like" action, dot. SC This is the second score corresponding to each like; SC is the number of screenshots. CP This is the second weight (e.g., 0.1) corresponding to the "like" action, dot. CP It's the second score corresponding to each like, and CP is the number of copies; w PA This is the second weight (e.g., 0.1) corresponding to the "like" action, dot. PAPA is the second score corresponding to each like, and PA is the number of times pasted. In this formula, the specific number of times refers to the number of times counted within the calculation time of the matching score.

[0583] Through the specific scoring mechanism and weight settings mentioned above, the server can more accurately evaluate the behavior of users under different objectives, and push the corresponding test function detection results accordingly, thereby optimizing the user experience and service quality.

[0584] Optionally, the system can also use the object's interactive behavior and emotional response to calculate the matching score. Emotional response refers to the results of sentiment analysis, such as pleasure level and focus level, which will not be elaborated on here.

[0585] Next, the server needs to determine the target completion rate of the user based on the matching score, and push the test function detection results based on the target completion rate.

[0586] Specifically, the server can set multiple thresholds for goal completion (such as not started, in progress, near completion, and completed) based on historical data and business experience. These thresholds are used to categorize the calculated matching scores, thereby accurately determining which goal completion stage the user is currently at. For example, when the matching score falls within the "near completion" threshold range, the server will determine that the user is about to achieve its set goal. It should be noted that different threshold standards can be set for different browsing goals.

[0587] Then, the server pushes relevant testing functions based on the goal completion rate to detect the results and collects feedback from users in order to further optimize the model, such as machine learning, deep learning, and reinforcement learning models. For example, for the goal of learning new knowledge, the server can push functions such as knowledge quizzes to help users check their learning effectiveness; while for goals of entertainment and relaxation or finding inspiration, evaluation can be carried out by using indicators such as user interaction and emotional response.

[0588] Through this comprehensive goal completion assessment and feedback mechanism, the server can effectively monitor and evaluate the goal achievement process of users, providing timely support and guidance, thereby significantly improving the overall user experience and goal achievement rate. Furthermore, the server can not only provide timely feedback on the user's progress but also continuously improve recommendation algorithms and models based on actual feedback, ensuring the provision of more accurate and personalized services.

[0589] Taking the vague goals of "relaxing and having fun" and "learning new knowledge" as examples, the following is the specific technical implementation logic and details of judging the user's goal completion level based on the matching score and pushing the test results based on the goal completion level:

[0590] First, based on historical data and business experience, specific threshold states are set for different objectives.

[0591] For example, for the goal of "relaxing and having fun," states such as "not relaxed at all," "starting to relax," "deep relaxation," and "fully relaxed" can be set; for the goal of "learning new knowledge," states such as "early stage of knowledge exploration," "in the process of knowledge accumulation," "approaching knowledge mastery," and "knowledge mastery completed" can be set. These thresholds are used to classify the user's state at different stages of goal completion.

[0592] Next, the calculated matching score is compared with a preset threshold to determine which goal completion stage the user is currently at. For "relaxing and having fun," the matching score is determined by comprehensively considering the user's viewing time, likes, shares, and sentiment analysis results. For "learning new knowledge," the matching score is determined by comprehensively considering the user's learning behavior (such as viewing time, number of interactions, note-taking, etc.).

[0593] For example, if the user's score in entertainment is below a certain threshold, it is judged as "completely not relaxed"; if the score in learning is close to a certain threshold, it is considered as "close to mastering knowledge".

[0594] Through this mechanism, the server can accurately assess the user's goal completion rate and push corresponding test functions or recommended content according to the specific stage, ensuring that users receive timely support and guidance, thereby improving the overall user experience and goal achievement rate.

[0595] Next, test functions are prepared in advance in the system backend to detect the results at different stages of goal completion. These results are carefully designed and optimized to help users better achieve their respective goals.

[0596] For example, for the goal of "relaxing and having fun", prepare different types of content recommendation lists (such as recommendations for light comedy movies, playlists of soothing music, etc.); for the goal of "learning new knowledge", prepare knowledge quizzes, learning mini-quizzes, etc., to test the user's mastery of the new knowledge.

[0597] Next, based on the user's current goal completion status, the system intelligently selects and pushes the corresponding test function detection results.

[0598] For example, if the user is in a "completely unrelaxed" state of "relaxing with entertainment," the system may push lighthearted and humorous short videos to stimulate their desire to relax; if they are in the "early stage of knowledge exploration" of "learning new knowledge," it will push engaging popular science articles or introductory learning resources; if they are close to completing their goal, for "relaxing with entertainment," it will push high-quality entertainment activity recommendations to consolidate the relaxation experience; for "learning new knowledge," it will push comprehensive knowledge challenges or in-depth discussion topics to consolidate learning outcomes.

[0599] Furthermore, during the aforementioned process, the server can also collect real-time feedback data from users regarding the push notification results, including click-through rate, viewing time, quiz accuracy, and satisfaction rating. This feedback data can be used for continuous optimization, adjusting matching algorithms, threshold settings, and push strategies to improve the accuracy of target completion assessments and the effectiveness of push notifications.

[0600] In this way, the server can not only provide timely and personalized testing functions to detect and reinforce the progress of users, but also continuously improve the model based on actual feedback, ensuring that users receive more accurate and effective services.

[0601] Optionally, this application supports pushing content browsing reports to users on the client side. Correspondingly, the server side needs to generate the content browsing report. One possible implementation is as follows:

[0602] The server collects browsing data from users within a specified time period; based on the browsing data, it generates a content browsing report and sends the report to the client; the client is used to display the content browsing report after responding to the report viewing operation.

[0603] The content browsing report includes, but is not limited to, at least one of the following: the browsing activity of the object within a specified time period; and subsequent browsing suggestions related to the object's browsing goals.

[0604] In this way, the server can not only provide timely feedback on the achievement of the user's goals, but also generate detailed browsing reports to help users better understand their browsing behavior and goal progress, thereby optimizing the future browsing experience.

[0605] Taking a specified time period as one day as an example, the content browsing report is the daily report. For details regarding the content and presentation of the content browsing report, please refer to the relevant content on the client side mentioned above, which will not be repeated here. The following are the specific technical implementation logic and details of the server providing feedback on the achievement of the user's target results and generating the user's browsing report for the day:

[0606] First, the server collects and analyzes data, specifically utilizing big data processing frameworks such as Hadoop and Spark to efficiently process and analyze the massive amounts of browsing data from users. The specific implementation method is as follows:

[0607] The server continuously collects user interaction data during browsing, including clicks, viewing time, likes, and comments. For the two goals of "relaxing and having fun" and "learning new knowledge," it records the interactive behaviors related to each goal, such as interactions with entertainment content and interactions with learning resources.

[0608] Furthermore, by combining sentiment analysis results and behavioral patterns, user engagement and satisfaction in "entertainment and relaxation" and "learning new knowledge" were assessed. NLP and sentiment analysis techniques were used to determine users' emotional states when browsing different types of content to ascertain their feelings about achieving their goals.

[0609] Next, a series of quantitative indicators are set for quantitative evaluation of the results, such as the total viewing time of videos and the number of interactions, to measure the users' goal completion rate. The brief process is as follows:

[0610] For example, for the goal of "relaxing and having fun," in addition to considering shared behaviors, indicators such as pleasure can also be considered; for the goal of "learning new knowledge," in addition to considering shared and specific behaviors, indicators such as focus and knowledge mastery can also be considered. The matching score for the user is calculated by combining the interactive behaviors listed above with these evaluation indicators. For example, a weighted average algorithm can be used to assign different weights to different indicators, and then comprehensively calculate the user's score for both goals.

[0611] The calculated matching score is compared with a preset threshold standard to determine the target completion rate (this process can be found in the above explanation of determining target completion rate based on matching score, and will not be repeated here). Corresponding feedback information can also be generated based on the target completion rate. For example, positive feedback or improvement suggestions can be given based on the user's completion status. If the user reaches the preset target score, positive feedback can be given, such as "You have successfully achieved your entertainment and relaxation goal for today!" or "You have made significant progress in learning new knowledge!" If not, improvement suggestions are provided, such as "It seems you still have some room for relaxation today; why not try the recommended content?" or "In terms of learning new knowledge, you can try exploring related topics more deeply." In addition, target detection content matching the target completion rate can be pushed (this process can be found in the above explanation of target detection content, and will not be repeated here).

[0612] Next, based on the above analysis, a content browsing report for the user on that day will be generated. The specific implementation method is as follows:

[0613] First, data integration is performed, collecting all browsing data from the user on that day, including pages visited, dwell time, and interactive behaviors. Browsing data related to the goals of "relaxing and having fun" and "learning new knowledge" is differentiated for targeted analysis. By integrating this data, a comprehensive browsing record can be created, categorizing and integrating browsing data under different goals to ensure that subsequent report generation clearly shows the user's activity across each goal.

[0614] The report content is then generated, including but not limited to the following parts:

[0615] Browsing Overview: Lists the main websites or applications visited by the user on that day, and the total browsing time (e.g., ...). Figure 15 (Section 1513 of 151). This section separately tracks the browsing time of users on entertainment and learning-related websites or applications, providing an overall overview of their browsing activity.

[0616] Interest point analysis: Identify the main interest points (e.g., based on the user's interactive behavior) Figure 15 (Part 152 of 1522) such as favorite video types, music styles, etc. Analyze the user's interests and preferences in entertainment and learning to help them better understand their areas of interest.

[0617] Emotional state: Summarize the emotional changes of users during the browsing process, such as pleasure, boredom, etc. (e.g.) Figure 15 (The emotional value in 153). Analyze the emotional state of users during entertainment and learning processes to understand their experience and feelings towards different goals.

[0618] Goal Achievement Status: Provides a detailed breakdown of user progress on the "Entertainment and Relaxation" and "Learning New Knowledge" goals, including specific scores and feedback. Providing detailed achievement data for both goals helps users understand their performance in different areas. For example... Figure 15 Parts 151 and 153, etc.

[0619] Finally, the generated content browsing report will be presented in a personalized manner, as detailed below:

[0620] Use visualization tools such as D3.js and Tableau to create intuitive charts and report interfaces. Customize the presentation of reports based on user preferences and historical data, such as using charts and color coding, to improve report readability and appeal.

[0621] It provides a summary and suggestions section to help users quickly understand their browsing activity and receive targeted advice. For example, for the goal of "relaxing and having fun," it provides relaxation suggestions and entertainment recommendations; for the goal of "learning new knowledge," it provides learning suggestions and relevant resource recommendations to help users better plan their future browsing goals and time allocation.

[0622] See Figure 18 The diagram shown is a timing diagram illustrating the interaction between a client and a server in an embodiment of this application. The specific implementation flow of this method is as follows: S1801~S1815:

[0623] S1801: Use objects to begin browsing and set the object browsing target.

[0624] In this step, the client will respond to several setting operations of the user object, guiding the user object to set the browsing target (subsequent detection target). For specific implementation details, please refer to the above embodiments, and repeated parts will not be described again.

[0625] S1802: The server understands the target set by the object.

[0626] In this step, the server will preprocess and extract features from the object browsing target set by the object to facilitate subsequent judgment on whether the target is clear. For specific implementation methods, please refer to the above embodiments, and repeated parts will not be described again.

[0627] S1803: The server determines whether the target is clear. If it is, it executes S1805; otherwise, it executes S1804.

[0628] In this step, the server determines the target clarity of the browsing target set by the user. For specific implementation details, please refer to the above embodiments, and repeated details will not be described again.

[0629] S1804: The client bootstrap uses an object to set a specific goal.

[0630] In this step, guidance can be provided through dialogue. For specific guidance methods, please refer to the above embodiments. Repeated details will not be repeated here.

[0631] S1805: Customized content related to the server matching target.

[0632] In this step, the server searches for recommended content; for details, please refer to the above embodiments. Repeated points will not be elaborated upon.

[0633] S1806: The server detects the interactive behavior of the objects being used.

[0634] In this step, the server detects the user's real-time interactive behavior and emotional reactions during the browsing process; for specific implementation details, please refer to the above embodiments. Repeated points will not be elaborated upon.

[0635] S1807: The server analyzes the interactive behavior and understanding of the users.

[0636] In this step, the server analyzes the level of understanding based on the detected interactive behaviors, etc. For specific implementation details, please refer to the above embodiments. Repeated points will not be repeated.

[0637] S1808: The server matches the interactive behavior of the user with the target.

[0638] In this step, the server matches the behavior of the user object with the target based on the analysis results, and finally obtains a matching score. For specific implementation details, please refer to the above embodiment. Repeated points will not be described again.

[0639] S1809: The server determines the target completion rate based on the matching score.

[0640] In this step, the server determines the current goal completion rate of the user based on a preset threshold and a matching score. For specific implementation details, please refer to the above embodiment. Repeated points will not be elaborated upon.

[0641] S1810: The server determines whether the target completion rate has been met. If it has, S1811 is executed; otherwise, S1813 is executed.

[0642] In this step, the server can analyze whether the target completion rate has reached 100% or other completion thresholds (such as 95%) to determine whether the target completion rate has been met. For specific implementation details, please refer to the above embodiments. Repeated points will not be described again.

[0643] S1811: Test the results of the client push interactive function.

[0644] In this step, the client can push interactive testing functions to the user based on the target detection content returned by the server that matches the current target completion level. For example, the client can use the test content to check the user's browsing results. For specific implementation details, please refer to the above embodiments. Repeated points will not be described again.

[0645] S1812: The server determines whether the test passes. If it does, it executes S1814; otherwise, it executes S1813.

[0646] In this step, the server can determine whether the test content passes by judging whether the score reaches a score threshold (such as 90 points in a percentage system).

[0647] S1813: The server reminds the user to continue learning and pushes content from associated targets.

[0648] In this step, the server continues to provide the user with relevant content to encourage them to continue learning.

[0649] S1814: The server provides feedback on the target results of the user.

[0650] S1815: The client displays a report on the content viewed that day.

[0651] Through S1814 and S1815, the server can provide feedback on the target results of the user and generate a content browsing report, which can then be pushed to the client for display.

[0652] It should be noted that, Figure 18 The interaction methods listed are merely simple examples, and this article does not impose any specific limitations on them.

[0653] In summary, the goal-oriented and intelligent time feedback system of this application has several significant effects. On the one hand, through goal setting, real-time monitoring and feedback, and time management optimization, users can clearly define broad, non-specific goals, avoid blindly browsing low-value content, improve time utilization efficiency, and ensure that users focus on goal achievement. On the other hand, the intelligent recommendation system combines multiple factors and uses advanced technology to provide diverse, personalized, and inspiring content recommendations, differentiating itself from traditional search engines and recommendation systems, reducing the user's cognitive burden, and avoiding one-size-fits-all recommendations; the content browsing report generation function provides users with detailed reports and personalized suggestions, optimizing future goals and time arrangements, and improving the depth of memory and learning effectiveness of "learning new knowledge." Simultaneously, it meets users' needs for easy, non-specific information access, provides new exploration methods for users in different states, and enriches users' experience and knowledge reserves.

[0654] Based on the same inventive concept, embodiments of this application also provide a content recommendation device. For example... Figure 19 As shown, this is a structural schematic diagram of the content recommendation device 1900, which may include:

[0655] The first setting unit 1901 is used to present a target setting interface in response to a first setting operation triggered by the current browsing interface, the target setting interface including: at least one preset browsing target;

[0656] The second setting unit 1902 is used to respond to a second setting operation triggered by the target setting interface and to present target prompt information in the target setting interface; the target prompt information is used to guide the user to browse the target by referring to the at least one preset browsing target setting.

[0657] The content display unit 1903 is used to present a content recommendation interface after the user sets the browsing target of the object. The content recommendation interface includes recommended content related to the browsing target of the object.

[0658] Optionally, the second setting unit 1902 is specifically used to perform at least one of the following operations:

[0659] The target setting interface displays a first target prompt message; wherein the first target prompt message includes: suggestions for setting the target.

[0660] In the target setting interface, a second target prompt message is presented in the form of a dialogue; wherein, the second target prompt message includes: a guiding question generated based on the user's current initial browsing target; the guiding question is used to guide the user to update the initial browsing target to the user's browsing target.

[0661] Optionally, there are multiple guidance questions; the second setting unit 1902 is specifically used for:

[0662] In the target setting interface, the second target prompt information is presented in the form of a multi-turn dialogue, wherein each round of dialogue corresponds to a guiding question, and the second target prompt information also includes: preset answer options corresponding to the guiding question.

[0663] Optionally, the content display unit 1903 is specifically used for:

[0664] After the user sets the browsing target, at least one content preview card is displayed in the target setting interface, and the at least one content preview card is dynamically updated during the recommended content search process. The at least one content preview card is generated based on the currently searched recommended content.

[0665] Once the preset search criteria are met, the user will be redirected to the content recommendation interface.

[0666] Optionally, the device further includes:

[0667] The reminder unit 1904 is used to present a browsing prompt message after detecting that the user has been continuously browsing secondary content for a preset time; wherein, the browsing prompt message is used to indicate that the secondary content is not related to the user's browsing target; the secondary content is content whose relevance to the user's browsing target is lower than a preset relevance threshold.

[0668] Optionally, the browsing prompt information further includes a target adjustment control; then the reminder unit 1904 is further used for:

[0669] In response to a third setting operation triggered by the target adjustment control, the adjusted object browsing target input by the object is determined, and the content recommendation interface is updated according to the adjusted object browsing target.

[0670] Optionally, the browsing prompt information further includes at least one content summary card, each content summary card corresponding to one of the recommended contents; the at least one content summary card is generated based on recommended content related to the browsing target of the object; then the reminder unit 1904 is further used for:

[0671] In response to the selection of a target content summary card in the at least one content summary card, the user is redirected to the content details interface of the recommended content corresponding to the target content summary card.

[0672] Optionally, the reminder unit 1904 is further configured to:

[0673] In response to a return operation triggered by the content details interface, return to the content recommendation interface; or

[0674] In response to a switching operation triggered through the content details interface, a content details interface for other recommended content is displayed.

[0675] Optionally, the device further includes:

[0676] The inquiry unit 1905 is used to present feedback prompt information after a preset reminder condition is met; wherein, the feedback prompt information is used to prompt the user to continue browsing the target object as before; the preset reminder condition includes at least one of the following:

[0677] The user resumes browsing after interrupting the browsing target.

[0678] The preset browsing time related to the object browsing target has been reached, but the object browsing target has not yet been completed.

[0679] Optionally, the device further includes:

[0680] The report display unit 1906 is used to present a viewing control for a content browsing report; wherein the content browsing report is generated based on the browsing data of the user within a specified time period;

[0681] In response to a report viewing operation triggered by the viewing control, the content browsing report is displayed; wherein the content browsing report includes at least one of the following:

[0682] The browsing activity of the user within the specified time period;

[0683] The suggestion of subsequent browsing related to the object browsing target.

[0684] Optionally, the content browsing report also includes tool controls; then the report display unit 1906 is further used for:

[0685] In response to a tool viewing operation triggered by the tool control, a tutorial on using an auxiliary tool related to the object browsing target is presented; wherein, the auxiliary tool is a tool used to assist the user in browsing the object during the browsing process related to the object browsing target.

[0686] Optionally, the content browsing report further includes a target return control; then the report display unit 1906 is further used for:

[0687] In response to a return operation triggered by the target return control, return to the content recommendation interface.

[0688] Optionally, the device further includes:

[0689] Detection unit 1907 is used to present target detection content that matches the current target completion level;

[0690] The target completion rate is determined based on the user's interactive behavior during the browsing of the recommended content; the target detection content includes at least one of the following:

[0691] Test content used to detect whether the user has completed the browsing target of the object;

[0692] Additional recommended content to support the user in achieving their browsing goals.

[0693] Based on the same inventive concept, embodiments of this application also provide a content recommendation device. For example... Figure 20 As shown, this is a structural schematic diagram of a content recommendation device 2000, which may include:

[0694] The first feedback unit 2001 is used to send target prompt information to the client after receiving a target setting request from the client; wherein, the client is used to present the target prompt information in the target setting interface; the target setting interface is presented by the client in response to a first setting operation triggered by the current browsing interface, and the target setting interface includes: at least one preset browsing target; the target prompt information is used to guide the user to set the browsing target by referring to the at least one preset browsing target;

[0695] The second feedback unit 2002 is used to search for recommended content related to the browsing target of the user after the user sets the browsing target of the user, and send the search results to the client; wherein the client is used to display the recommended content through the content recommendation interface.

[0696] Optionally, the first feedback unit 2001 is specifically used for:

[0697] The initial browsing target included in the target setting request is obtained; the initial browsing target is set by the user based on the target setting interface.

[0698] Check whether the initial browsing target is clear;

[0699] If it is determined that the initial browsing goal is unclear, a guidance question is generated based on the initial browsing goal, and the guidance question is sent to the client through the goal prompt information; wherein, the guidance question is used to guide the user to update the initial browsing goal to the user's browsing goal.

[0700] Optionally, if the search results include first recommended content and second recommended content, then the second feedback unit 2002 is specifically used for:

[0701] Based on the historical feature information of the user and the browsing target of the user, search for first recommended content related to the browsing target of the user, and send the first recommended content to the client;

[0702] Based on the user's interaction characteristics with the currently recommended content and the user's browsing goal, search for second recommended content related to the user's browsing goal, and send the second recommended content to the client.

[0703] Optionally, the expected browsing time for the second recommended content is determined based on the interaction feature information and the object browsing goal.

[0704] Optionally, the device further includes:

[0705] The detection unit 2003 is used to detect the user's interaction behavior with the currently recommended content and determine the matching score between the interaction behavior and the user's browsing target;

[0706] The matching score is compared with a preset completion threshold to obtain the current target completion level.

[0707] Based on the target completion rate, corresponding target detection content is sent to the client; wherein the target detection content includes at least one of the following:

[0708] Test content used to detect whether the user has completed the browsing target of the object;

[0709] Additional recommended content to support the user in achieving their browsing goals.

[0710] Optionally, the device further includes:

[0711] The reminder unit 2004 is used to send a browsing reminder message to the client after detecting that the user has been continuously browsing secondary content for a preset time; wherein, the browsing reminder message is used to indicate that the secondary content is not related to the user's browsing target; the secondary content is content whose relevance to the user's browsing target is lower than a preset relevance threshold.

[0712] Optionally, the device further includes:

[0713] The report generation unit 2005 is used to collect browsing data of the user within a specified time period;

[0714] Based on the browsing data, a content browsing report is generated; wherein the content browsing report includes at least one of the following: the browsing status of the user within the specified time period; and subsequent browsing suggestions related to the user's browsing goals;

[0715] The content browsing report is sent to the client; wherein the client is configured to display the content browsing report after responding to the report viewing operation.

[0716] For ease of description, the above sections are divided into modules (or units) according to their functions and described separately. Of course, in implementing this application, the functions of each module (or unit) can be implemented in one or more software or hardware components.

[0717] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0718] Having described the content recommendation method and apparatus of the exemplary embodiments of this application, the electronic device according to another exemplary embodiment of this application will now be described.

[0719] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0720] Based on the same inventive concept as the above-described method embodiments, this application also provides an electronic device. In one embodiment, the electronic device may be a server, such as... Figure 1 The server 120 is shown. In this embodiment, the structure of the electronic device can be as follows: Figure 21 As shown, it includes a memory 2101, a communication module 2103, and one or more processors 2102.

[0721] The memory 2101 is used to store computer programs executed by the processor 2102. The memory 2101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0722] Memory 2101 may be volatile memory, such as random-access memory (RAM); memory 2101 may also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 2101 may be any other medium capable of carrying or storing a desired computer program having the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 2101 may be a combination of the above-described memories.

[0723] Processor 2102 may include one or more central processing units (CPUs) or digital processing units, etc. Processor 2102 is used to implement the recommended method described above when calling computer programs stored in memory 2101.

[0724] The communication module 2103 is used to communicate with terminal devices and other servers.

[0725] This application embodiment does not limit the specific connection medium between the memory 2101, communication module 2103, and processor 2102. This application embodiment... Figure 21 The memory 2101 and the processor 2102 are connected via a bus 2104, and the bus 2104 is in Figure 21 The diagram uses thick lines to describe the connections between other components; these are for illustrative purposes only and should not be considered limiting. The 2104 bus can be divided into address bus, data bus, control bus, etc. For ease of description, Figure 21 It is described using only a thick line, but does not indicate that there is only one bus or one type of bus.

[0726] The memory 2101 stores a computer storage medium, which stores computer-executable instructions for implementing the content recommendation method of this application embodiment. The processor 2102 is used to execute the above-described content recommendation method, such as... Figure 17 As shown.

[0727] In another embodiment, the electronic device may also be other electronic devices, such as... Figure 1 The terminal device 110 is shown. In this embodiment, the electronic device can be structured as follows: Figure 22 As shown, it includes components such as: communication component 2210, memory 2220, display unit 2230, camera 2240, sensor 2250, audio circuit 2260, Bluetooth module 2270, processor 2280, etc.

[0728] The communication component 2210 is used to communicate with the server. In some embodiments, it may include a Circuit-Wireless Fidelity (WiFi) module, which is a short-range wireless transmission technology. Electronic devices can use the WiFi module to help users send and receive information.

[0729] The memory 2220 can be used to store software programs and data. The processor 2280 executes various functions of the terminal device 110 and performs data processing by running the software programs or data stored in the memory 2220. The memory 2220 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. The memory 2220 stores an operating system that enables the terminal device 110 to run. In this application, the memory 2220 may store the operating system and various application programs, and may also store computer programs that execute the methods recommended in the embodiments of this application.

[0730] The display unit 2230 can also be used to display information input by the user or information provided to the user, as well as various menus of the terminal device 110, in a graphical user interface (GUI). Specifically, the display unit 2230 may include a display screen 2232 disposed on the front of the terminal device 110. The display screen 2232 may be configured as a liquid crystal display, a light-emitting diode, or the like. The display unit 2230 can be used to display the current browsing interface, target setting interface, etc., as shown in the embodiments of this application.

[0731] The display unit 2230 can also be used to receive input digital or character information and generate signal inputs related to user settings and function control of the terminal device 110. Specifically, the display unit 2230 may include a touch screen 2231 disposed on the front of the terminal device 110, which can collect touch operations of the user on or near it, such as clicking buttons, dragging scroll boxes, etc.

[0732] The touchscreen 2231 can be placed on top of the display screen 2232, or the touchscreen 2231 and the display screen 2232 can be integrated to realize the input and output functions of the terminal device 110. After integration, it can be referred to as a touch display screen. In this application, the display unit 2230 can display the application and the corresponding operation steps.

[0733] Camera 2240 can be used to capture still images, which users can then share via an application. There can be one or multiple cameras 2240. An object is projected onto a photosensitive element through a lens, generating an optical image. This photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then transmitted to a processor 2280 to be converted into a digital image signal.

[0734] The terminal device may also include at least one sensor 2250, such as an accelerometer 2251, a proximity sensor 2252, a fingerprint sensor 2253, and a temperature sensor 2254. The terminal device may also be equipped with other sensors such as a gyroscope, barometer, hygrometer, thermometer, infrared sensor, light sensor, and motion sensor.

[0735] Audio circuitry 2260, speaker 2261, and microphone 2262 provide an audio interface between the user and terminal device 110. Audio circuitry 2260 converts received audio data into electrical signals, which are then transmitted to speaker 2261, where they are converted into sound signals for output. Terminal device 110 may also be equipped with volume buttons for adjusting the volume of the sound signal. On the other hand, microphone 2262 converts collected sound signals into electrical signals, which are received by audio circuitry 2260, converted into audio data, and then output to communication component 2210 for transmission to, for example, another terminal device 110, or to memory 2220 for further processing.

[0736] Bluetooth module 2270 is used to interact with other Bluetooth devices that also have Bluetooth modules via the Bluetooth protocol. For example, a terminal device can establish a Bluetooth connection with a wearable electronic device (such as a smartwatch) that also has a Bluetooth module through Bluetooth module 2270, thereby exchanging data.

[0737] The processor 2280 is the control center of the terminal device, connecting various parts of the terminal through various interfaces and lines. It executes various functions and processes data by running or executing software programs stored in the memory 2220 and calling data stored in the memory 2220. In some embodiments, the processor 2280 may include one or more processing units; the processor 2280 may also integrate an application processor and a baseband processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the baseband processor mainly handles wireless communication. It is understood that the baseband processor may not be integrated into the processor 2280. In this application, the processor 2280 can run the operating system, applications, user interface display and touch response, as well as the content recommendation method of the embodiments of this application. Furthermore, the processor 2280 is coupled to the display unit 2230.

[0738] In some possible implementations, various aspects of the content recommendation method provided in this application can also be implemented in the form of a program product, which includes a computer program. When the program product is run on an electronic device, the computer program causes the electronic device to perform the steps of the content recommendation method according to the various exemplary embodiments of this application described above. For example, the electronic device can perform actions such as... Figure 2 or Figure 17 The steps are shown in the figure.

[0739] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0740] The program product of the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on an electronic device. However, the program product of this application is not limited thereto. In this document, the readable storage medium may be any tangible medium that contains or stores a program that may be used by or in conjunction with a command execution system, apparatus, or device.

[0741] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a readable computer program. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with a command execution system, apparatus, or device.

[0742] Computer programs contained on readable media may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0743] Computer programs for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The computer program can execute entirely on the user's electronic device, partially on the user's electronic device, as a standalone software package, partially on the user's electronic device and partially on a remote electronic device, or entirely on a remote electronic device or server. In cases involving remote electronic devices, the remote electronic device can be connected to the user's electronic device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external electronic device (e.g., via the Internet using an Internet service provider).

[0744] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0745] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0746] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing a computer-usable computer program.

[0747] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0748] These computer program commands may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the commands stored in the computer-readable storage medium produce an article of manufacture including command means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0749] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0750] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A content recommendation method characterized by, The method includes: In response to a first setting operation triggered by the current browsing interface, a target setting interface is presented, the target setting interface including: at least one preset browsing target; In response to a second setting operation triggered through the target setting interface, target prompt information is displayed in the target setting interface; the target prompt information is used to guide the user to browse the target by referring to the at least one preset browsing target setting. After the user sets the browsing target, a content recommendation interface is presented, which includes recommended content related to the browsing target.

2. The method of claim 1, wherein, The presentation of target prompt information in the target setting interface includes at least one of the following: The target setting interface displays a first target prompt message; wherein the first target prompt message includes: suggestions for setting the target. In the target setting interface, a second target prompt message is presented in the form of a dialogue; wherein, the second target prompt message includes: a guiding question generated based on the user's current initial browsing target; the guiding question is used to guide the user to update the initial browsing target to the user's browsing target.

3. The method of claim 2, wherein, The guidance questions are multiple; the second target prompt information is presented in the target setting interface in the form of a dialogue, including: In the target setting interface, the second target prompt information is presented in the form of a multi-turn dialogue, wherein each round of dialogue corresponds to a guiding question, and the second target prompt information also includes: preset answer options corresponding to the guiding question.

4. The method of claim 1, wherein, After the user sets the browsing target, a content recommendation interface is presented, including: After the user sets the browsing target, at least one content preview card is displayed in the target setting interface, and the at least one content preview card is dynamically updated during the recommended content search process. The at least one content preview card is generated based on the currently searched recommended content. Once the preset search criteria are met, the user will be redirected to the content recommendation interface.

5. The method of claim 1, wherein, The method further includes: After detecting that the user has been continuously browsing secondary content for a preset period of time, a browsing prompt message is displayed; wherein, the browsing prompt message is used to indicate that the secondary content is not related to the user's browsing target; the secondary content is content whose relevance to the user's browsing target is lower than a preset relevance threshold.

6. The method of claim 5, wherein, The browsing prompt information also includes a target adjustment control; therefore, the method further includes: In response to a third setting operation triggered by the target adjustment control, the adjusted object browsing target input by the object is determined, and the content recommendation interface is updated according to the adjusted object browsing target.

7. The method of claim 5, wherein, The browsing prompt information also includes at least one content summary card, and each content summary card corresponds to one of the recommended contents; The at least one content summary card is generated based on recommended content related to the browsing target of the object; then the method further includes: In response to the selection of a target content summary card in the at least one content summary card, the user is redirected to the content details interface of the recommended content corresponding to the target content summary card.

8. The method of claim 7, wherein, The method further includes: In response to a return operation triggered by the content details interface, return to the content recommendation interface; or In response to a switching operation triggered through the content details interface, a content details interface for other recommended content is displayed.

9. The method according to any one of claims 1 to 8, characterized in that, The method further includes: Upon meeting preset reminder conditions, a feedback prompt is displayed; wherein, the feedback prompt is used to prompt the user to continue browsing the target object as before; the preset reminder conditions include at least one of the following: The user resumes browsing after interrupting the browsing target. The preset browsing time related to the object browsing target has been reached, but the object browsing target has not yet been completed.

10. The method according to any one of claims 1 to 8, characterized in that, The method further includes: A view control for presenting a content browsing report; wherein the content browsing report is generated based on the browsing data of the user within a specified time period; In response to a report viewing operation triggered by the viewing control, the content browsing report is displayed; wherein the content browsing report includes at least one of the following: The browsing activity of the user within the specified time period; The suggestion of subsequent browsing related to the object browsing target.

11. The method of claim 10, wherein, The content browsing report also includes tool controls; therefore, the method further includes: In response to a tool viewing operation triggered by the tool control, a tutorial on using an auxiliary tool related to the object browsing target is presented; wherein, the auxiliary tool is a tool used to assist the user in browsing the object during the browsing process related to the object browsing target.

12. The method of claim 10, wherein, The content browsing report also includes a target return control; therefore, the method further includes: In response to a return operation triggered by the target return control, return to the content recommendation interface.

13. The method according to any one of claims 1 to 8, wherein The method further includes: Based on the current goal completion rate, target detection content matching the goal completion rate is presented; wherein, the goal completion rate is determined based on the user's interactive behavior during the browsing of the recommended content; the target detection content includes at least one of the following: Test content used to detect whether the user has completed the browsing target of the object; Additional recommended content to support the user in achieving their browsing goals.

14. A content recommendation method characterized by, The method includes: Upon receiving a target setting request from a client, a target prompt message is sent to the client; wherein, the client is used to present the target prompt message in a target setting interface; the target setting interface is presented by the client in response to a first setting operation triggered by the current browsing interface, and the target setting interface includes: at least one preset browsing target; the target prompt message is used to guide the user to set the browsing target by referring to the at least one preset browsing target; After the user sets the browsing target, recommended content related to the browsing target is searched, and the search results are sent to the client; wherein, the client is used to display the recommended content through a content recommendation interface.

15. The method of claim 14, wherein, After receiving the target setting request from the client, the system sends target prompt information to the client, including: The initial browsing target included in the target setting request is obtained; the initial browsing target is set by the user based on the target setting interface. Check whether the initial browsing target is clear; If it is determined that the initial browsing goal is unclear, a guidance question is generated based on the initial browsing goal, and the guidance question is sent to the client through the goal prompt information; wherein, the guidance question is used to guide the user to update the initial browsing goal to the user's browsing goal.

16. The method of claim 14, wherein, The search results include first recommended content and second recommended content. The search involves finding recommended content related to the browsing target of the object and sending the search results to the client, including: Based on the historical feature information of the user and the browsing target of the user, search for first recommended content related to the browsing target of the user, and send the first recommended content to the client; Based on the user's interaction characteristics with the currently recommended content and the user's browsing goal, search for second recommended content related to the user's browsing goal, and send the second recommended content to the client.

17. The method of claim 16, wherein, The expected browsing time for the second recommended content is determined based on the interactive feature information and the object's browsing goal.

18. The method according to any one of claims 14 to 17, characterized in that, The method further includes: Detect the user's interaction behavior with the currently recommended content, and determine the matching score between the interaction behavior and the user's browsing target; The matching score is compared with a preset completion threshold to obtain the current target completion level. Based on the target completion rate, corresponding target detection content is sent to the client; wherein the target detection content includes at least one of the following: Test content used to detect whether the user has completed the browsing target of the object; Additional recommended content to support the user in achieving their browsing goals.

19. The method of any one of claims 14 to 17, wherein, The method further includes: After detecting that the user has been continuously browsing secondary content for a preset duration, a browsing prompt message is sent to the client; wherein, the browsing prompt message is used to indicate that the secondary content is not related to the user's browsing target; the secondary content is content whose relevance to the user's browsing target is lower than a preset relevance threshold.

20. The method of any one of claims 14 to 17, wherein, The method further includes: Collect browsing data of the user within a specified time period; Based on the browsing data, a content browsing report is generated; wherein the content browsing report includes at least one of the following: the browsing status of the user within the specified time period; and subsequent browsing suggestions related to the user's browsing goals; The content browsing report is sent to the client; wherein the client is configured to display the content browsing report after responding to the report viewing operation.

21. A content recommendation apparatus characterized by comprising: include: The first setting unit is used to present a target setting interface in response to a first setting operation triggered by the current browsing interface, the target setting interface including: at least one preset browsing target; The second setting unit is used to respond to a second setting operation triggered through the target setting interface and to present target prompt information in the target setting interface; the target prompt information is used to guide the user to browse the target by referring to the at least one preset browsing target setting. The content display unit is used to present a content recommendation interface after the user sets the browsing target of the object. The content recommendation interface includes recommended content related to the browsing target of the object.

22. A content recommendation apparatus characterized by comprising: include: The first feedback unit is used to send target prompt information to the client after receiving a target setting request from the client; wherein, the client is used to present the target prompt information in the target setting interface; the target setting interface is presented by the client in response to a first setting operation triggered by the current browsing interface, and the target setting interface includes: at least one preset browsing target; the target prompt information is used to guide the user to set the browsing target by referring to the at least one preset browsing target; The second feedback unit is used to search for recommended content related to the browsing target after the user sets the browsing target, and send the search results to the client; wherein the client is used to display the recommended content through a content recommendation interface.

23. An electronic device, comprising: It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of any of the methods described in claims 1 to 20.

24. A computer-readable storage medium, characterized in that, It includes a computer program that, when run on an electronic device, causes the electronic device to perform the steps of any of the methods described in claims 1 to 20.

25. A computer program product, characterised in that, The method includes a computer program stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, causing the electronic device to perform the steps of any one of claims 1 to 20.

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