Information interaction method and system of large-screen terminal, electronic equipment and storage medium

Abstract

The invention relates to an information interaction method of a large-screen terminal, which solves the problems of deep interaction level, complicated operation, low efficiency and lack of individuation of content recommendation of the large-screen terminal in the related technology by constructing a first card area guided to interact by an AI (artificial intelligence) agent and a second card area for displaying personalized recommendation content. According to the method, long-term and short-term historical interaction data and real-time session context of a user are combined, a highly personalized content card is generated and displayed, intelligent interaction mechanisms such as hovering trigger preview, multi-mode feedback and emotion recognition are introduced, and a passive operation mode of the user is converted into an intelligent service mode of active recommendation. According to the method and the device, the path for the user to discover the content is remarkably shortened, the key operation is greatly simplified, and the convenience, the efficiency and the intelligent level of user interaction are improved, so that the overall visual experience and the satisfaction degree of the user are optimized.

Description

Technical Field

[0001] This application relates to the field of intelligent control of large-screen terminals, and in particular to a method, system, computer device and computer-readable storage medium for information interaction on large-screen terminals. Background Technology

[0002] Large-screen terminals generally adopt a grid-based card-style desktop layout as the basic architecture of the user interface. This architecture presents content entry points such as applications, videos, and news in the form of icons or cards arranged in a matrix on the main screen.

[0003] In this type of layout, users need to use a physical remote control or virtual directional keys to move the focus, and then perform multiple selections using the "up, down, left, right" keys and triggering actions with the "confirm" button to access the target content level by level. This cumbersome operation method significantly contradicts the display size advantage that large-screen terminals should possess, severely impacting user efficiency. Furthermore, due to the space limitations of the grid layout, the content effectively displayed on a single screen is typically limited to no more than nine items (e.g., in a 3x3 layout), forcing a large amount of content to be folded into secondary pages, resulting in a flattened hierarchy of core information and a longer path for users to obtain the desired content.

[0004] Currently, no effective solution has been proposed to address the issue of inefficient user interaction mechanisms provided by large-screen terminals in related technologies. Summary of the Invention

[0005] This application provides an information interaction method, system, computer device, and computer-readable storage medium for a large-screen terminal, in order to at least solve the problem of poor efficiency of the user interaction mechanism provided by the large-screen terminal in the related art.

[0006] In a first aspect, embodiments of this application provide an information interaction method for a large-screen terminal, the method comprising: Upon receiving an activation command, the system responds by displaying an initial interactive interface. This initial interactive interface includes a first card area and a second card area, wherein: The first card area displays multiple interactive guides generated by the AI ​​agent; The second card area includes at least one content card, which displays personalized recommended content generated by a content recommendation model based on the user's historical interaction data.

[0007] In some embodiments, the second card area includes a first content card and a second content card; The first content card displays the first personalized recommendation content, which is generated based on the user's long-term historical interaction records and preference feature data; The second content card displays a second personalized recommendation, which is generated based on the conversation context. The conversation context is the dialogue history data, viewing behavior data, and search behavior data generated by the user's interaction with the large-screen terminal within a short preset period.

[0008] In some embodiments, after displaying the first content card and the second content card, the method further includes: Monitor user interaction with the first content card or the second content card; Determine whether the interactive behavior meets the preset triggering conditions. If so, trigger a multimedia preview associated with the personalized recommendation content. The multimedia preview is manifested in: playing highlights of the personalized recommended content on the first content card or the second content card, and / or displaying a graphic summary of the personalized recommended content.

[0009] In some embodiments, after displaying the first content card and the second content card, the method further includes: Monitor user interaction with the first content card or the second content card; Determine whether the interactive behavior meets the preset triggering conditions. If so, trigger a multimedia preview associated with the personalized recommendation content. The multimedia preview is manifested in the following way: in the first card area, multiple interactive guides associated with the personalized recommendation content are displayed.

[0010] In some embodiments, the interactive behavior includes: voice interaction behavior and remote control interaction behavior; When the interaction behavior is a remote control interaction behavior, the remote control focus dwell time of the first content card or the second content card is detected, and it is determined whether the focus dwell time is greater than a first preset threshold. If so, a multimedia preview associated with the personalized recommended content is triggered. If the interaction is a voice interaction, determine whether the text information corresponding to the voice interaction contains a query keyword that matches the personalized recommendation content. If so, trigger a multimedia preview associated with the personalized recommendation content.

[0011] In some embodiments, after triggering a multimedia preview associated with the personalized recommendation content, the method further includes: Real-time monitoring of user feedback and interaction behavior with the first card area or the second card area, including: voice interaction behavior and remote control interaction behavior; Execute corresponding response operations based on the feedback interaction behavior, including: If a confirmation command is detected, the user will be redirected to the full display page corresponding to the personalized recommendation content, where the content details and operation options will be displayed. If a cancellation command is detected, the multimedia preview will stop and the user will return to the initial interactive interface. If a collection command is detected, the personalized recommended content is added to the user's collection list, and the preference weight of the current personalized recommended content in the content recommendation model is increased. If the feedback interaction command is not detected within the first preset time period, the multimedia preview is stopped, and the preference weight of the current personalized recommended content in the content recommendation model is reduced.

[0012] In some embodiments, after triggering a multimedia preview associated with the personalized recommendation content, the method further includes: Real-time monitoring of user feedback interaction behavior on the first card area or the second card area, wherein the feedback interaction behavior is voice interaction behavior; Extract voiceprint features from the voice commands corresponding to the feedback interaction behavior; By inputting the voiceprint features into a pre-trained emotion recognition model, the model outputs emotion classification results, which include excitement and disgust. If the output result is the excited emotion, increase the weight parameters of the current personalized content and its similar content in the content recommendation model, extend the duration of the multimedia preview, and highlight the guiding interactive content associated with the current personalized recommendation content in the first card area. If the output result is the stated aversion, terminate the current multimedia preview and reduce the weight parameters of the content recommendation model for the current personalized content and its similar content.

[0013] In some embodiments, after displaying multiple pieces of guided interactive content generated by an AI agent in the first card area, the method further includes; The interactive guidance content is refreshed at a second preset time interval; The system monitors the user's input of interactive commands based on the guided interactive content, and generates corresponding personalized recommendation content through the AI ​​agent based on the input of interactive commands based on the guided interactive content. The interactive commands are generated based on voice interaction behavior or remote control interaction behavior.

[0014] Secondly, embodiments of this application provide an information interaction system for a large-screen terminal, the system comprising: an acquisition module and a response module, wherein, The acquisition module is used to acquire the start command; The response module is used to respond to an activation command and display an initial interactive interface, wherein the initial interactive interface sets a first card area and a second card area, wherein: The first card area displays multiple interactive guides generated by the AI ​​agent; The second card area includes at least one content card, which displays personalized recommended content generated by a content recommendation model based on the user's historical interaction data.

[0015] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0017] Compared to related technologies, this application provides an information interaction method for large-screen terminals. By constructing a first card area guided by an AI agent and a second card area displaying personalized recommended content, it solves the problems of deep interaction layers, cumbersome operations, low efficiency, and lack of personalized content recommendations in related technologies. Combining users' long-term and short-term historical interaction data with real-time conversation context, it generates and displays highly personalized content cards and introduces intelligent interaction mechanisms such as hover-triggered preview, multimodal feedback, and emotion recognition, transforming the user's passive operation mode into an active recommendation intelligent service mode. This significantly shortens the path for users to discover content, greatly simplifies button operations, and improves the convenience, efficiency, and intelligence level of user interaction, thereby optimizing the user's overall visual experience and satisfaction. Attached Figure Description

[0018] 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: Figure 1 This is a flowchart of an information interaction method for a large-screen terminal according to an embodiment of this application; Figure 2 This is a schematic diagram of an initial interactive interface according to an embodiment of this application.

[0019] Figure 3 This is a structural block diagram of an information interaction system for a large-screen terminal according to an embodiment of this application; Figure 4 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0021] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0022] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0023] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0024] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "a," "an," "an," "the," and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms "comprising," "including," "having," and any variations thereof used in this application are intended to cover non-exclusive inclusion. The terms "first," "second," etc., used in this application are merely to distinguish similar objects and do not represent a specific ordering of objects.

[0025] Currently, large-screen terminals such as projectors and smart TVs generally adopt a grid-based card-style desktop layout. Users need to use the remote control to select multiple directions using the directional keys and click the confirmation button to access the target content, resulting in a long operation path and low efficiency. At the same time, limited screen space leads to a large amount of content being collapsed into secondary pages, and the system lacks a dynamic content prediction and recommendation mechanism based on user profiles and real-time intent. To address the problems of poor efficiency and low personalization in the user interaction mechanisms of large-screen terminals in related technologies, this application provides an information interaction method, system, computer device, and computer-readable storage medium for large-screen terminals.

[0026] This solution can be applied to various large-screen terminal devices such as projectors, smart TVs, digital advertising screens, and conference screens.

[0027] In one specific embodiment Figure 1 This is a flowchart of an information interaction method for a large-screen terminal according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps: S101, obtain the start command, and respond to the start command to display the initial interactive interface.

[0028] In this embodiment, the activation command can be triggered by the user in various ways, such as pressing a specific function key on the remote control (such as the "Home" or "AI" key), through voice commands (such as "Open AI Desktop"), or automatically entering the interactive interface after the system starts up.

[0029] Upon receiving the activation command, the processor of the large-screen terminal loads and displays a specially designed initial interactive interface. Figure 2 This is a schematic diagram of an initial interactive interface according to an embodiment of this application, such as... Figure 2 As shown, the interface is divided into a first card area and a second card area in terms of layout.

[0030] The first card area displays multiple guided interactive content pieces generated by an AI agent. This AI agent is the core interaction hub of the system and can be an advanced conversational AI built into the large-screen terminal's operating system or a large third-party language model accessed via a cloud API. The content displayed in this area can be dynamically generated guided questions or engaging topics by the AI ​​agent, such as "What kind of movie do you want to watch today?", "Let's play a guessing game; guess correctly to receive a free trial of member content", or "What's a classic line from this movie?". This content aims to proactively and guidedly stimulate user interaction, changing the traditional desktop's passive waiting-for-user mode.

[0031] The second card area includes at least one content card, used to display personalized recommendations generated by a content recommendation model based on the user's historical interaction data. This area is the core of achieving precise content delivery; the content displayed is highly personalized, aiming to prioritize content that users may be interested in, thus shortening the user's discovery path.

[0032] S102, strategically differentiate and display personalized recommended content within the second card area.

[0033] To improve the accuracy and diversity of recommendations, the second card area in this embodiment is further subdivided into at least two content cards, namely the first content card and the second content card, which are generated using different recommendation strategies.

[0034] The first content card displays personalized recommendations based on the user's long-term historical interaction records and preference characteristics. The content recommendation model deeply analyzes the user's long-term data to build a stable, multi-dimensional user profile. This data includes, but is not limited to: historical viewing records (e.g., the user watched more than 30% of science fiction films in the past year), application usage frequency, preferences for specific actors or directors, and viewing habits at different times (such as weekend evenings). Based on this profile, the first content card recommends content that highly matches the user's stable interests; for example, prioritizing a recently released, highly-rated science fiction film for science fiction fans.

[0035] The second content card displays personalized recommendations generated based on the conversation context. This conversation context refers to short-term, real-time user behavior data, such as the user's conversation history with the AI ​​agent, recently viewed content, or search queries performed during the current device startup period. This strategy boasts extremely high timeliness and relevance. For example, if a user has just searched for and watched the movie *The Wandering Earth 2* via voice search, the second content card might immediately recommend "other works by director Guo Fan," "film and television series adapted from Liu Cixin's science fiction novels," or "highly rated hard science fiction classics of the same genre." This recommendation based on instant context accurately captures the user's current interests, enabling an intelligent experience for watching shows and exploring content.

[0036] S103, monitor user interaction behavior and trigger multimedia preview; After the initial interactive interface is displayed, the user's interaction behavior with the first or second content card is continuously monitored. These behaviors include remote control interaction behavior and voice interaction behavior.

[0037] When the detected interactive behavior meets the preset trigger conditions, the system will activate the multimedia preview function associated with the personalized recommended content.

[0038] In remote control interaction scenarios, the preset trigger condition can be the duration of the remote control focus stay. For example, when a user moves the remote control cursor to a content card and stays there for more than a preset threshold (e.g., 2 seconds), the system will determine that the user has shown initial interest in the content and automatically trigger a preview. This "hover preview" design eliminates the need for the user to click the "OK" button, greatly improving the smoothness and efficiency of the interaction.

[0039] In voice interaction scenarios, the preset trigger condition can be keyword matching in the voice command. For example, when the focus is on a movie card, if the user says "Introduce this movie" or "Who is in it?", the voice recognition and natural language understanding will determine that the command is related to the current card content, and a preview will be triggered.

[0040] Furthermore, multimedia previews can be presented in various formats. One format is to directly play a highlight clip of the recommended content (such as a 15-second trailer clip) or display a text and image summary within the current content card. Another format is for an AI agent to display multiple new interactive prompts related to the recommended content in the first card area, such as "Want to know about the behind-the-scenes stories of 'The Wandering Earth 2'?" or "Let's talk about the scientific settings of this movie." In this embodiment, this cross-regional linkage enriches the dimensions of interaction.

[0041] S104, execute the corresponding response operation based on the user's feedback interaction behavior.

[0042] After the multimedia preview is triggered, the system will continue to monitor the user's subsequent feedback and interactions in real time.

[0043] If a user's confirmation command is detected (such as pressing the "OK" button on the remote control or saying "play"), the system will immediately jump to the full details page of the content or start playing directly. The page will provide more detailed information and operation options (such as selecting episodes, viewing the cast list, etc.).

[0044] If a cancellation command is detected (such as pressing the "Back" button or saying "Exit"), the system will stop the preview and the interface will return to its initial state.

[0045] If a save command is detected (such as pressing a specific function key or saying "save"), the system will add the content to the user's personal save list and send a strong positive feedback signal to the content recommendation model, increasing the preference weight of the content and its related features (such as the same type or the same actor) for more accurate recommendations in the future.

[0046] If no user interaction is detected within a preset period (e.g., within 10 seconds after the preview ends), the system will determine that the user's interest is low, automatically stop the preview, send a weak negative feedback signal to the recommendation model, and appropriately reduce the recommendation weight of the content in the future. It can be understood that this learning from user "non-operation" behavior is also an important basis for model optimization in this embodiment.

[0047] In a more advanced embodiment, the system can integrate emotion recognition functionality to achieve a deeper level of interactive understanding. When a user provides voice feedback in preview activation mode, the technical solution of this application not only understands the literal meaning but also analyzes the voiceprint characteristics (such as tone, speech rate, energy, etc.).

[0048] Furthermore, a pre-trained emotion recognition model (e.g., based on a deep neural network) can be used to determine the emotions contained in a user's speech, such as "excitement" or "disgust".

[0049] If excitement is detected (e.g., a user says "This is great!" in an enthusiastic tone), it will be treated as a very strong positive feedback: the recommendation weight of this content and similar content will be significantly increased, the preview time will be extended, and related fun questions and answers will be highlighted in the first card area.

[0050] If aversion is detected (e.g., a user saying "This again" in an impatient tone), it will be treated as strong negative feedback: the preview will be immediately terminated, and the recommendation weight of that content and similar content will be significantly reduced to prevent it from being recommended again in the short term. In this way, the system can capture more subtle emotional preferences of users, making the iterative optimization of the recommendation model more intelligent and human-like.

[0051] Through steps S101 to S104 above, this application provides an information interaction method for a large-screen terminal. By constructing a first card area guided by an AI agent and a second card area displaying personalized recommended content, it solves the problems of deep interaction layers, cumbersome operation, low efficiency, and lack of personalized content recommendations in related technologies. This solution combines the user's long-term and short-term historical interaction data with real-time conversation context to generate and display highly personalized content cards. It also introduces intelligent interaction mechanisms such as hover-triggered preview, multimodal feedback, and emotion recognition, transforming the user's passive operation mode into an active recommendation intelligent service mode. This significantly shortens the path for users to discover content, greatly simplifies button operations, and improves the convenience, efficiency, and intelligence level of user interaction, thereby optimizing the user's overall visual experience and satisfaction.

[0052] This application also provides an information interaction system for a large-screen terminal. Figure 3 This is a structural block diagram of an information interaction system for a large-screen terminal according to an embodiment of this application, such as... Figure 3 As shown, the system includes: an acquisition module 30 and a response module 31, wherein: The acquisition module 30 is used to acquire the user's activation command and subsequent various interactive behavior commands; In this embodiment, the activation command can be triggered by the user in various ways, such as pressing a specific function key on the remote control (such as the "Home" or "AI" key), through voice commands (such as "Open AI Desktop"), or automatically entering the interactive interface after the system starts up.

[0053] The response module 31 is used to display the initial interactive interface according to the instructions, generate and display recommendation cards with different strategies, and trigger multimedia preview and execute feedback response.

[0054] Specifically, upon responding to the activation command, the processor of the large-screen terminal loads and displays a specially designed initial interactive interface. This interface is divided into a first card area and a second card area in terms of layout.

[0055] The first card area displays multiple guided interactive content generated by an AI agent. This AI agent is the core interaction hub of the system and can be an advanced conversational AI built into the large-screen terminal's operating system or a large third-party language model accessed via a cloud API. The content displayed in this area can be dynamically generated guided questions or engaging topics by the AI ​​agent, such as "What kind of movie do you want to watch today?", "Let's play a guessing game; guess correctly and you'll get free access to member content," or "Test your knowledge: what's a classic line from this movie?" This content aims to proactively and guidedly stimulate user interaction, changing the traditional desktop's passive waiting-for-user mode.

[0056] The second card area includes at least one content card, used to display personalized recommendations generated by a content recommendation model based on the user's historical interaction data. This area is the core of achieving precise content delivery; the content displayed is highly personalized, aiming to prioritize content that users may be interested in, thus shortening the user's discovery path.

[0057] This system constructs a first card area guided by an AI agent for interaction and a second card area displaying personalized recommendations, solving the problems of deep interaction layers, cumbersome operations, low efficiency, and lack of personalized content recommendations on large-screen terminals in related technologies. This system combines users' long-term and short-term historical interaction data with real-time conversation context to generate and display highly personalized content cards. It also introduces intelligent interaction mechanisms such as hover-triggered preview, multimodal feedback, and emotion recognition, transforming the user's passive operation mode into a proactive recommendation intelligent service mode. This significantly shortens the path for users to discover content, greatly simplifies button operations, and improves the convenience, efficiency, and intelligence level of user interaction, thereby optimizing the overall visual experience and user satisfaction.

[0058] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0059] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0060] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program: S1, Obtain the activation command, respond to the activation command to display the initial interactive interface, and set the first card area and the second card area in the initial interactive interface: S2, the first card area, displays multiple interactive guides generated by the AI ​​agent; S3, the second card area includes at least one content card, which displays personalized recommended content generated by a content recommendation model based on the user's historical interaction data.

[0061] In one embodiment, Figure 4 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 4 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 4 As shown, the electronic device includes a processor, a network interface, internal memory, and non-volatile memory connected via an internal bus. The non-volatile memory stores an operating system, computer programs, and a database. The processor provides computing and control capabilities, the network interface communicates with external terminals via a network connection, the internal memory provides an environment for the operating system to run, the computer programs are executed by the processor to implement an information interaction method for a large-screen terminal, and the database stores data.

[0062] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0063] Furthermore, in conjunction with the information interaction methods of the large-screen terminal in the above embodiments, this application embodiment can provide a storage medium for implementation. The storage medium stores a computer program; when the computer program is executed by a processor, it implements any of the information interaction methods of the large-screen terminal in the above embodiments.

[0064] In one embodiment, a computer program product is provided, comprising a computer program, characterized in that, when executed by a processor, the computer program implements any of the information interaction methods for large-screen terminals described in the above embodiments.

[0065] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0066] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0067] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An information interaction method for a large-screen terminal, characterized in that, The method includes: Upon receiving an activation command, the system responds by displaying an initial interactive interface. This initial interactive interface includes a first card area and a second card area, wherein: The first card area displays multiple interactive guides generated by the AI ​​agent; The second card area includes at least one content card, which displays personalized recommended content generated by a content recommendation model based on the user's historical interaction data.

2. The method according to claim 1, characterized in that, The second card area includes a first content card and a second content card; The first content card displays the first personalized recommendation content, which is generated based on the user's long-term historical interaction records and preference feature data; The second content card displays a second personalized recommendation, which is generated based on the conversation context. The conversation context is the dialogue history data, viewing behavior data, and search behavior data generated by the user's interaction with the large-screen terminal within a short preset period.

3. The method according to claim 2, characterized in that, After displaying the first content card and the second content card, the method further includes: Monitor user interaction with the first content card or the second content card; Determine whether the interactive behavior meets the preset triggering conditions. If so, trigger a multimedia preview associated with the personalized recommendation content. The multimedia preview is manifested in: playing highlights of the personalized recommended content on the first content card or the second content card, and / or displaying a graphic summary of the personalized recommended content.

4. The method according to claim 2, characterized in that, After displaying the first content card and the second content card, the method further includes: Monitor user interaction with the first content card or the second content card; Determine whether the interactive behavior meets the preset triggering conditions. If so, trigger a multimedia preview associated with the personalized recommendation content. The multimedia preview is manifested in the following way: in the first card area, multiple interactive guides associated with the personalized recommendation content are displayed.

5. The method according to any one of claims 3 and 4, characterized in that, The interactive behaviors include: voice interaction behaviors and remote control interaction behaviors; When the interaction behavior is a remote control interaction behavior, the remote control focus dwell time of the first content card or the second content card is detected, and it is determined whether the focus dwell time is greater than a first preset threshold. If so, a multimedia preview associated with the personalized recommended content is triggered. If the interaction is a voice interaction, determine whether the text information corresponding to the voice interaction contains a query keyword that matches the personalized recommendation content. If so, trigger a multimedia preview associated with the personalized recommendation content.

6. The method according to claim 5, characterized in that, After triggering a multimedia preview associated with the personalized recommended content, the method further includes: Real-time monitoring of user feedback and interaction behavior with the first card area or the second card area, including: voice interaction behavior and remote control interaction behavior; Execute corresponding response operations based on the feedback interaction behavior, including: If a confirmation command is detected, the user will be redirected to the full display page corresponding to the personalized recommendation content, where the content details and operation options will be displayed. If a cancellation command is detected, the multimedia preview will stop and the user will return to the initial interactive interface. If a collection command is detected, the personalized recommended content is added to the user's collection list, and the preference weight of the current personalized recommended content in the content recommendation model is increased. If the feedback interaction command is not detected within the first preset time period, the multimedia preview is stopped, and the preference weight of the current personalized recommended content in the content recommendation model is reduced.

7. The method according to claim 5, characterized in that, After triggering a multimedia preview associated with the personalized recommended content, the method further includes: Real-time monitoring of user feedback interaction behavior on the first card area or the second card area, wherein the feedback interaction behavior is voice interaction behavior; Extract voiceprint features from the voice commands corresponding to the feedback interaction behavior; By inputting the voiceprint features into a pre-trained emotion recognition model, the model outputs emotion classification results, which include excitement and disgust. If the output result is the excited emotion, increase the weight parameters of the current personalized content and its similar content in the content recommendation model, extend the duration of the multimedia preview, and highlight the guiding interactive content associated with the current personalized recommendation content in the first card area. If the output result is the stated aversion, terminate the current multimedia preview and reduce the weight parameters of the content recommendation model for the current personalized content and its similar content.

8. The method according to claim 1, characterized in that, After displaying multiple pieces of guided interactive content generated by the AI ​​agent in the first card area, the method further includes: The interactive guidance content is refreshed at a second preset time interval; The system monitors the user's input of interactive commands based on the guided interactive content, and generates corresponding personalized recommendation content through the AI ​​agent based on the input of interactive commands based on the guided interactive content. The interactive commands are generated based on voice interaction behavior or remote control interaction behavior.

9. An information interaction system for a large-screen terminal, characterized in that, The system includes: an acquisition module and a response module, wherein, The acquisition module is used to acquire the start command; The response module is used to respond to an activation command and display an initial interactive interface, wherein the initial interactive interface sets a first card area and a second card area, wherein: The first card area displays multiple interactive guides generated by the AI ​​agent; The second card area includes at least one content card, which displays personalized recommended content generated by a content recommendation model based on the user's historical interaction data.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

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