Label data processing method and device, electronic equipment, storage medium and product

Abstract

The present disclosure relates to a marking data processing method and device, electronic equipment, storage medium and product. In a first time period, first marking information is received, the first marking information comprising at least one marking data, the marking data being used to prompt a service state of a corresponding service; in a second time period, the first marking information is sent to a marking server, so that the marking server makes a display decision on the marking data in the first marking information, and obtains second marking information to be displayed, the second marking information comprising at least one marking data; in a displayed page currently in the second time period, the marking data in the second marking information is displayed. The present disclosure can avoid interface information overload and disturbance to user experience caused by indiscriminate display of a large amount of marking data.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to methods, apparatus, electronic devices, storage media, and products for processing marked data. Background Technology

[0002] With the development of internet technology, various functions can be integrated into applications. In order to help users understand the status of each function in a timely manner, applications can display corresponding tag information for each function. For example, the small red dot displayed on the page of a short video application is a typical tag information.

[0003] However, with the increase in functions, the large-scale, indiscriminate display of labeled information may lead to problems such as user attention being distracted, information overload, and visual fatigue, thereby reducing the user experience. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, storage medium, and product for processing labeled data, to solve at least one of the aforementioned technical problems. The technical solution of this disclosure is as follows: According to a first aspect of the present disclosure, a method for processing labeled data is provided, comprising: In the first time period, first tagging information is received, the first tagging information including at least one tagging data, the tagging data being used to indicate the service status of the corresponding service; In the second time period, the first tagging information is sent to the tagging server so that the tagging server makes a display decision on the tagging data in the first tagging information and obtains the second tagging information to be displayed, the second tagging information including at least one tagging data; In the currently displayed page during the second time period, the tag data from the second tag information is displayed.

[0005] In one exemplary embodiment, the method further includes: Send a tag information retrieval request to the tagging server so that the tagging server sends a tag data retrieval request to the service provider corresponding to each service, makes a display decision on the tag data returned by each service provider, generates and returns third tag information, the third tag information being part of the first tag information; or, Receive tagging data pushed by any of the service providers, wherein the tagging data belongs to the first tagging information.

[0006] In one exemplary implementation, the tagging server makes display decisions using the following method: Determine the remaining exposure quota corresponding to each of the marker data in the first marker information; Determine the target marker data that meets the exposure requirements with the remaining exposure allowance; Based on the display recommendation rate corresponding to the target tag data, the second tag information to be displayed is determined.

[0007] In one exemplary implementation, determining the remaining exposure quota corresponding to each of the marked data in the first marking information includes performing the following operations for each of the marked data: Based on the account characteristics of the target account, the display preference degree corresponding to the tagged data is determined. The display preference degree is used to indicate the degree of display preference of the target account for the prompt information of the business to which the tagged data belongs. The target account is the account used to display the tagged data. Based on the display preference, predict the total exposure amount corresponding to the labeled data; Based on the historical exposure data of the service and the total exposure amount, the remaining exposure amount of the labeled data is determined.

[0008] In one exemplary implementation, the account characteristics of the target account include at least one of the following: The target account's static attribute characteristics, the target account's device characteristics, and the target account's interactive behavior characteristics.

[0009] In one exemplary implementation, determining the second tag information to be displayed based on the display recommendation score corresponding to the target tag data includes: For each target tagged data, a corresponding display recommendation score is determined based on the target tagged data interaction rate and the target tagged data interaction quality. The target tagged data interaction rate indicates the target account's tagged data interaction rate with the business corresponding to the target tagged data, and the tagged data interaction quality indicates the target account's tagged data interaction quality with the business corresponding to the target tagged data. The target-labeled data is sorted in descending order of recommendation level to obtain a sorted list; The second tagging information is determined based on the sorted list.

[0010] In one exemplary implementation, determining the corresponding display recommendation score based on the target tag data interaction rate and the target tag data interaction quality includes: Obtain a target relationship graph, which indicates the interaction relationship between an account and tagged data. The horizontal axis of the target relationship graph represents the rate of interaction of the account with tagged data, and the vertical axis represents the quality of interaction of the account with tagged data. Based on the target tag data interaction rate and the target tag data interaction quality, the position of the target account in the target relationship graph is determined; Based on the location, the corresponding display recommendation level is determined.

[0011] In one exemplary implementation, prior to obtaining the target relationship graph, the method further includes: The average interaction rate is determined based on the interaction rate of the marked data for each of the aforementioned accounts; The average interaction quality is determined based on the interaction quality of the tagged data for each account; The center of the target relationship graph is determined based on the average interaction rate and the average interaction quality. Based on the center, the horizontal axis, and the vertical axis, a target relationship graph comprising four quadrants is generated. Each quadrant corresponds to a set of parameters, which are used to calculate the display recommendation degree.

[0012] In one exemplary implementation, determining the corresponding display recommendation score based on the location includes: Determine the target quadrant in the target relationship diagram where the location is situated; The display recommendation score is calculated based on the target parameter set corresponding to the target quadrant.

[0013] In one exemplary implementation, the target parameter set includes a first parameter, a second parameter, and a third parameter. Calculating the display recommendation score based on the target parameter set corresponding to the target quadrant includes: Based on the target account's interaction with the business corresponding to the target tag data, and the first parameter, a first recommendation component is determined; Based on the first page position of the currently displayed page in the second time period, the second page position of the business corresponding to the target tag data, and the second parameter, a second recommendation component is determined; Based on the remaining exposure quota corresponding to the target marker data and the third parameter, the third recommendation component is determined; The display recommendation score is calculated based on the first recommendation score component, the second recommendation score component, and the third recommendation score component.

[0014] In one exemplary implementation, determining the second tag information based on the sorted list includes: Obtain the tag data display quota K corresponding to the page currently being displayed in the second time period; In the sorted list, the first K labeled data are selected to obtain the second labeled information.

[0015] In one exemplary embodiment, after displaying the tag data in the second tag information, the method further includes: Update the remaining exposure quota corresponding to the tag data in the second tag information.

[0016] According to a second aspect of the present disclosure, a tag data processing apparatus is provided, comprising: The tag data receiving module is used to receive first tag information in a first time period, the first tag information including at least one tag data, the tag data being used to indicate the service status of the corresponding service; The tag data processing module is used to send the first tag information to the tag server in the second time period, so that the tag server makes a display decision on the tag data in the first tag information and obtains the second tag information to be displayed, wherein the second tag information includes at least one tag data. The tag data display module is used to display the tag data in the second tag information on the page currently being displayed during the second time period.

[0017] In one exemplary embodiment, the tag data receiving module is configured to: Send a tag information retrieval request to the tagging server so that the tagging server sends a tag data retrieval request to the service provider corresponding to each service, makes a display decision on the tag data returned by each service provider, generates and returns third tag information, the third tag information being part of the first tag information; or, Receive tagging data pushed by any of the service providers, wherein the tagging data belongs to the first tagging information.

[0018] In one exemplary embodiment, the tag data processing module is configured to: Determine the remaining exposure quota corresponding to each of the marker data in the first marker information; Determine the target marker data that meets the exposure requirements with the remaining exposure allowance; Based on the display recommendation rate corresponding to the target tag data, the second tag information to be displayed is determined.

[0019] In one exemplary embodiment, the tag data processing module is configured to: Based on the account characteristics of the target account, the display preference degree corresponding to the tagged data is determined. The display preference degree is used to indicate the degree of display preference of the target account for the prompt information of the business to which the tagged data belongs. The target account is the account used to display the tagged data. Based on the display preference, predict the total exposure amount corresponding to the labeled data; Based on the historical exposure data of the service and the total exposure amount, the remaining exposure amount of the labeled data is determined.

[0020] In one exemplary implementation, the account characteristics of the target account include at least one of the following: The target account's static attribute characteristics, the target account's device characteristics, and the target account's interactive behavior characteristics.

[0021] In one exemplary embodiment, the tag data processing module is configured to: For each target tagged data, a corresponding display recommendation score is determined based on the target tagged data interaction rate and the target tagged data interaction quality. The target tagged data interaction rate indicates the target account's tagged data interaction rate with the business corresponding to the target tagged data, and the tagged data interaction quality indicates the target account's tagged data interaction quality with the business corresponding to the target tagged data. The target-labeled data is sorted in descending order of recommendation level to obtain a sorted list; The second tagging information is determined based on the sorted list.

[0022] In one exemplary embodiment, the tag data processing module is configured to: Obtain a target relationship graph, which indicates the interaction relationship between an account and tagged data. The horizontal axis of the target relationship graph represents the rate of interaction of the account with tagged data, and the vertical axis represents the quality of interaction of the account with tagged data. Based on the target tag data interaction rate and the target tag data interaction quality, the position of the target account in the target relationship graph is determined; Based on the location, the corresponding display recommendation level is determined.

[0023] In one exemplary embodiment, the tag data processing module is configured to: The average interaction rate is determined based on the interaction rate of the marked data for each of the aforementioned accounts; The average interaction quality is determined based on the interaction quality of the tagged data for each account; The center of the target relationship graph is determined based on the average interaction rate and the average interaction quality. Based on the center, the horizontal axis, and the vertical axis, a target relationship graph comprising four quadrants is generated. Each quadrant corresponds to a set of parameters, which are used to calculate the display recommendation degree.

[0024] In one exemplary embodiment, the tag data processing module is configured to: Determine the target quadrant in the target relationship diagram where the location is situated; The display recommendation score is calculated based on the target parameter set corresponding to the target quadrant.

[0025] In one exemplary embodiment, the target parameter set includes a first parameter, a second parameter, and a third parameter, and the tag data processing module is configured to: Based on the target account's interaction with the business corresponding to the target tag data, and the first parameter, a first recommendation component is determined; Based on the first page position of the currently displayed page in the second time period, the second page position of the business corresponding to the target tag data, and the second parameter, a second recommendation component is determined; Based on the remaining exposure quota corresponding to the target marker data and the third parameter, the third recommendation component is determined; The display recommendation score is calculated based on the first recommendation score component, the second recommendation score component, and the third recommendation score component.

[0026] In one exemplary embodiment, the tag data processing module is configured to: Obtain the tag data display quota K corresponding to the page currently being displayed in the second time period; In the sorted list, the first K labeled data are selected to obtain the second labeled information.

[0027] In one exemplary embodiment, the tag data processing module is configured to: Update the remaining exposure quota corresponding to the tag data in the second tag information.

[0028] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the tag data processing method as described above.

[0029] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the tag data processing method as described above.

[0030] According to a fifth aspect of the present disclosure, a computer program product is provided, the computer program product including a computer program stored in a readable storage medium, wherein at least one processor of a computer device reads from the readable storage medium and executes the computer program, causing the device to perform the above-described tag data processing method.

[0031] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: The tagged data processing method, apparatus, electronic device, storage medium, and product disclosed herein can process tagged data differently in different time periods. Specifically, in the first time period, tagged data can be received to obtain first tagging information, but it is not displayed. Instead, after the second time period arrives, the first tagging information is sent to a tagging server for display decision-making, thereby intelligently determining the tagged data that needs to be displayed, and incorporating this tagged data into second tagging information and sending it to the client, whereby the client displays this tagged data in a timely manner.

[0032] This design avoids information overload and user disruption caused by indiscriminately displaying a large amount of all tagged data. Furthermore, because the tagged data displayed in the second phase is based on the tagging server's intelligent analysis and display decisions regarding the first tagged information, it ensures that the tagged data is more relevant to the user, more easily accepted, and thus improves the user experience.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0035] Figure 1 This is a schematic diagram illustrating the implementation environment of an application according to an exemplary embodiment.

[0036] Figure 2 This is a flowchart illustrating a labeled data processing method according to an exemplary embodiment.

[0037] Figure 3 This is a diagram illustrating a tag data processing framework according to an exemplary embodiment.

[0038] Figure 4 This is a schematic diagram illustrating a process of time-segmented tagged data processing according to an exemplary embodiment.

[0039] Figure 5 This is a schematic diagram illustrating the decision-making process of a tagging server according to an exemplary embodiment.

[0040] Figure 6 This is a schematic diagram illustrating the process of making display decisions on tagged data on the tagging server side according to an exemplary embodiment.

[0041] Figure 7 This is a block diagram of a tag data processing apparatus according to an exemplary embodiment.

[0042] Figure 8 This is a block diagram illustrating an electronic device for tag data processing according to an exemplary embodiment.

[0043] Figure 9 This is another block diagram of an electronic device for tagging data processing, according to an exemplary embodiment. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0045] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0046] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0047] Please see Figure 1 The illustration shows a schematic diagram of the implementation environment of the application provided in this embodiment of the present disclosure. The implementation environment may include at least one tag data processing terminal 110 and a tag server 120, which can communicate with each other via a network.

[0048] Specifically, the tag data processing terminal 110 interacts with the user through interaction with the tag server 120. Specifically, the tag data processing terminal 110 may receive first tag information in a first time period, the first tag information including at least one tag data, the tag data being used to indicate the business status of the corresponding service; in a second time period, the first tag information is sent to the tag server 120, so that the tag server 120 makes a display decision on the tag data in the first tag information to obtain second tag information to be displayed, the second tag information including at least one tag data; the tag data processing terminal 110 displays the tag data in the second tag information on the page currently being displayed in the second time period.

[0049] The tag data processing terminal 110 can communicate with the tag server 120 based on a browser / server (B / S) or client / server (C / S) model. The tag data processing terminal 110 may include physical devices such as smartphones, tablets, laptops, digital assistants, smart wearable devices, in-vehicle terminals, and servers, and may also include software running on the physical device, such as applications. The operating system running on the tag data processing terminal 110 in this embodiment may include, but is not limited to, Android, iOS, Linux, and Windows.

[0050] The tagging server 120 and the tagging data processing terminal 110 can establish and display a communication connection via wired or wireless means. The tagging server 120 may include a stand-alone server, a distributed server, or a server cluster consisting of multiple servers, wherein the server may be a cloud server.

[0051] Please refer to Figure 2 The diagram illustrates a flowchart of a tag data processing method in an exemplary embodiment of this disclosure. The execution entity of this method can be the aforementioned tag data processing terminal or tag server, or both in combination. Please refer to [link / reference] for details. Figure 2 The method may include: S210. In a first time period, receive first tag information, the first tag information including at least one tag data, the tag data being used to indicate the service status of the corresponding service.

[0052] In this embodiment, the marked data refers to a type of prompting information that can be used to indicate the business status of a corresponding service. The application can integrate various business modules, each corresponding to different business functions, such as an order management module, an inventory management module, and a customer management module. When the business status in a certain business module changes, the marked data can be used to provide a prompt, allowing users to promptly obtain the latest status of the relevant business.

[0053] Specifically, data can be presented in various forms, including but not limited to visual elements such as numerical badges, text labels, color codes, and blinking icons. For example, in the order management module, when there are new orders pending processing, a red numerical badge can be displayed in the upper right corner of the module's entry icon to indicate the number of orders pending processing; in the inventory management module, when the inventory of a certain product is lower than a preset threshold, the corresponding inventory status of the product can be marked with a yellow warning label to prompt managers to replenish the stock in time; in the customer management module, when there are important customers that need to be followed up, the customer's name can be highlighted or marked with a special icon in the customer list.

[0054] Taking short video applications as an example, these applications may integrate various tabs corresponding to different business functions on their pages. For instance, the top navigation bar might display tabs such as "Local," "Mall," "Life," "Following," "Live," and "Discover," while the bottom navigation bar might display tabs such as "Home," "Friends," "Make Money," and "Me." These tabs may display small red dots, red numbers, or red text dots; these all constitute labeled data. In the initial period, the client (terminal) of the short video application does not display the labeled data received during this initial period but rather collects it to obtain the initial labeled information.

[0055] During the first time period, if any business activity experiences a change in its status or generates a new status, corresponding tagged data may be pushed to the client. Taking a typical short video application scenario as an example, when a user receives a new private message, the "Friends" business status changes, and tagged data containing the number of unread messages is pushed to the client; when the platform issues limited-time coupons to users, the "Mall" business generates a new status of "New Coupon Received," and corresponding tagged data is pushed; when a user's followed streamer starts a live stream, the "Live Stream" business status changes, and corresponding tagged data is pushed; when the platform sends virtual items, the "Earn Money" business generates a new status, and corresponding tagged data is pushed to the client; when a new user posts nearby activity in the "Local" business, corresponding tagged data is pushed. All this tagged data carries a business identifier, which indicates the association between the business and the corresponding tagged data.

[0056] The first tagging information reflects the cumulative changes in the status of each business within the first time period. It should be noted that the duration of the first time period can be flexibly set according to the actual application scenario, such as 5 minutes, 15 minutes, or 30 minutes. The specific duration can be configured by default or customized by the user. During the first time period, the client can continuously receive tagging data pushed from various business modules, but it does not immediately render and display it on the interface. Instead, it temporarily stores this tagging data in the local cache or memory, forming the first tagging information.

[0057] S220. In the second time period, the first tag information is sent to the tag server so that the tag server makes a display decision on the tag data in the first tag information to obtain the second tag information to be displayed, the second tag information including at least one tag data.

[0058] The first and second time periods alternate. The duration of the second time period can be flexibly set according to the actual application scenario, such as 5 minutes, 15 minutes, or 30 minutes. The specific duration can be configured by default or customized by the user. During the second time period, the client packages the first tagging information accumulated in the first time period and sends it to the tagging server. After receiving the first tagging information, the tagging server first parses it, extracting the various tag data and their corresponding business identifiers contained within. Subsequently, the tagging server makes a display decision, determines the tagging information that needs to be displayed, and then generates second tagging information, which is fed back to the client so that the client can display the tagging data in the second time period.

[0059] If new labeled data is received during the second time period, this labeled data can be displayed directly, or it can be sent to the labeling server for display decision, and the decision result can be fed back to the client for display.

[0060] In this embodiment, the first time period is equivalent to a "red light period," during which the client is in a data collection state. Although various business modules may continuously generate and push marked data, the client only performs receiving and temporary storage operations and does not render or display the marked data on the interface, ensuring that the business status change information is fully accumulated without interrupting the user's current application experience. The second time period is equivalent to a "green light period," during which the client selectively renders the marked data received in the first stage by sending the accumulated first marked information to the marking server. The first and second time periods alternate in a cycle, forming a periodic processing mechanism of "collection-decision-display," which avoids interface flickering or information overload caused by high-frequency status changes and ensures timely display of changes in business status information.

[0061] S230. On the page currently displayed during the second time period, display the tag data from the second tag information.

[0062] In the currently displayed page during the second time period, the tagged data determined by the tagging server is presented to the user. This embodiment of the disclosure does not limit the display method of the tagged data, and different display methods can be used for the tagged data of different services.

[0063] Taking the short video application mentioned earlier as an example, for some services, a small red dot can indicate a new service status, while for others, specific numbers can be displayed. These numbers can represent the number of new messages or the number of pending tasks within that service. For instance, in message notification services, the data can be displayed as the number of unread messages; in friend request services, it can be displayed as the number of friend requests awaiting review; and in event reminder services, it can be displayed as the countdown days or hours until the event starts.

[0064] Taking a live streaming application as an example, the tag data for the "gift" service can be displayed as a dynamic animation that slides in from the top of the screen and fades out automatically after a few seconds; or, for the "anchor follow reminder" service, the tag data can be designed as a light interactive form where an avatar bubble pops out from the edge of the screen, and when the user clicks it, the bubble expands to display the follower's nickname and the time of following.

[0065] In this disclosure, the tagged data can be processed differently in different time periods. Specifically, in the first time period, the tagged data can be received to obtain the first tagging information, but it is not displayed. Instead, after the second time period arrives, the first tagging information is sent to the tagging server for display decision-making, thereby intelligently determining the tagged data that needs to be displayed, and incorporating this tagged data into the second tagging information and sending it to the client, whereby the client displays this tagged data in a timely manner.

[0066] This design avoids information overload and user disruption caused by indiscriminately displaying a large amount of all tagged data. Furthermore, because the tagged data displayed in the second phase is based on the tagging server's intelligent analysis and display decisions regarding the first tagged information, it ensures that the tagged data is more relevant to the user, more easily accepted, and thus improves the user experience.

[0067] In one exemplary embodiment, the method further includes: Send a tag information retrieval request to the tagging server so that the tagging server sends a tag data retrieval request to the service provider corresponding to each service, makes a display decision on the tag data returned by each service provider, generates and returns third tag information, the third tag information being part of the first tag information; or, Receive tagging data pushed by any of the service providers, wherein the tagging data belongs to the first tagging information.

[0068] In this implementation, the method of sending a tag information retrieval request to the tagging server embodies a proactive data retrieval mechanism. Under this mechanism, the tagging server acts as an intermediate coordination layer, interfacing with multiple service providers. When a client initiates a tag information retrieval request to the tagging server, the tagging server immediately sends tag data retrieval requests in parallel or serially to the service providers corresponding to each service, collecting tag data from multiple service dimensions. Subsequently, the tagging server makes a display decision and generates third-party tag information to feed back to the client. The advantage of this approach is that the tagging server can centrally manage the aggregation and processing of multi-source data, avoiding the increased network overhead and complexity caused by the client directly establishing connections with numerous service providers. Simultaneously, the unified decision-making of the tagging server ensures the quality and consistency of the displayed tag data.

[0069] In this implementation, a passive data acquisition mechanism is established by receiving tagged data pushed by any service provider. Under this mechanism, the service provider proactively pushes tagged data to the client based on its own business logic or triggering conditions, without waiting for query requests. This approach is suitable for business scenarios with high real-time requirements, reduces the pressure of periodic polling, and makes resource consumption more balanced.

[0070] Please refer to Figure 3 The diagram illustrates a tag data processing framework according to an exemplary embodiment of this disclosure. The framework comprises two core modes: pull links and push links.

[0071] 1. Pull link (client actively initiates a request) The client actively "pulls" the tagged data, while the tagging server and business provider passively respond, assemble the data, and return it to the client for rendering.

[0072] During application cold start: The client can retrieve tagging data generated by multiple service providers in batches by interacting with the tagging server. The tagging server assembles this tagging data and may also filter the data based on display decisions (optional). Then it obtains the tagging information and returns this tagging information to the client. If the client is in the "green light" state, it can render directly. If the client is in the "red light" state, it can wait. Usually, during application cold start, the client is in the "green light" state and can render directly.

[0073] In addition, after the application starts, the client can also poll at fixed time intervals to request tag data from the corresponding service provider individually.

[0074] 2. Push Link (marking server-initiated push) After the application starts, the tagging server can also proactively "push" tagging data to the client. For example, some tagging data with high real-time requirements needs to be notified to the user immediately, which can be pushed using a long-lived link.

[0075] The client also includes a rendering component, which is used to display the marked data on the page after the three stages of data acquisition → display decision → marked data display.

[0076] The Klink service in the tagging server is used to maintain a long-lived connection channel with the client, enabling real-time push of tagging data.

[0077] Please refer to Figure 4 This diagram illustrates a time-segmented tagging data processing procedure in an exemplary embodiment of this disclosure. During a cold start, the application starts, and upon an active request from the client, the tagging server receives tagging data from service providers A, B, and C. After passing a display decision, the server allows the display of tagging data from service providers A and C. Therefore, the client can display the tagging data from service providers A and C and enter an online state.

[0078] In online mode, during the first time period (red light period), the client can receive tagging data for services D and E pushed by the service provider, but does not directly execute exposure; instead, it triggers temporary storage logic. When entering the second time period (green light period), the client obtains the first tagging information based on the stored tagging data and sends it to the tagging server. The tagging server makes a display decision, determining to discard D and expose E, which the client then executes. After the green light phase, it can re-enter the red light phase, alternating between the two.

[0079] The process of processing the marked data essentially embodies the "red light temporary storage + green light release" model. This model can reasonably control the display of marked data, avoid concentrated bursts, and also control the page display effect, thereby optimizing the user experience.

[0080] In one exemplary implementation, please refer to Figure 5 This diagram illustrates the display decision-making process of the tagging server in this disclosure. The tagging server makes display decisions through the following method: S510. Determine the remaining exposure quota corresponding to each of the marker data in the first marker information.

[0081] Remaining exposure quota refers to the maximum number of times the tagged data has not yet been displayed. Specifically, each service's tagged data can be allocated an initial exposure quota when the application starts. As the number of times the tagged data corresponding to that service has been actually exposed in historical periods increases, its remaining exposure quota decreases accordingly.

[0082] In one exemplary implementation, determining the remaining exposure quota corresponding to each of the marked data in the first marking information includes performing the following operations for each of the marked data: S511. Based on the account characteristics of the target account, determine the display preference degree corresponding to the marked data. The display preference degree is used to indicate the degree of display preference of the target account for the prompt information of the business to which the marked data belongs. The target account is the account used to display the marked data.

[0083] In one exemplary implementation, the account characteristics of the target account include at least one of the following: the account static attribute characteristics of the target account, the device characteristics of the target account, and the interaction behavior characteristics of the target account.

[0084] Static account attributes refer to the relatively stable basic information formed during the registration and use of a target account, including but not limited to account registration duration, membership level, real-name authentication status, age range, geographical distribution, and industry type. These characteristics can depict the basic profile of the target account from a macro perspective, providing a basis for judging its potential acceptance of labeled data for specific businesses. For example, accounts with longer registration durations and higher membership levels usually have a deeper understanding of platform functions and may show a higher tolerance and willingness to explore labeled data for innovative businesses; while accounts in specific regions or industries may show a significant preference for labeled data for certain vertical businesses due to localization needs or industry characteristics.

[0085] Device characteristics encompass information related to the target account's current login and historical terminal devices, including device model, operating system type, screen resolution, network connection method, and usage time distribution. Device characteristics may be related to the characteristics of the display medium for prompts and the user's contextual usage habits. For example, users of high-end devices may be more sensitive to visually appealing labeled data, while users of smaller screen devices prefer simpler, more intuitive pages and have lower tolerance for labeled data.

[0086] Interactive behavior characteristics refer to various dynamic operation data generated by the target account within the application, covering page browsing history, function click frequency, exposure click-through rate of historical tagged data, and user behavior after tagged data is clicked. Interactive behavior characteristics can reflect the target account's true interests, preferences, and behavioral decision-making patterns. Through in-depth mining of interactive behavior characteristics, the target account's display needs for tagged data for specific business purposes can be identified, thereby effectively protecting the user experience.

[0087] Determining display preferences based on the aforementioned multidimensional features can improve the rationality of the allocation of exposure quotas for each business's labeled data, thereby maximizing the effective reach of labeled data under exposure constraints, improving reach, and reducing unnecessary disturbance to accounts with low preferences, achieving a dual optimization of operational efficiency and user experience.

[0088] S512. Based on the display preference, predict the total exposure amount corresponding to the labeled data.

[0089] This disclosure does not limit the prediction method; for example, multiple regression prediction models, convolutional neural networks, large language models, or time-series-based prediction models can be used. The implementation process of this prediction method can be achieved in various ways by those skilled in the art, and will not be elaborated upon here. There is a positive correlation between display preference and total exposure; that is, the higher the display preference, the greater the predicted total exposure, and vice versa.

[0090] S513. Determine the remaining exposure quota of the marked data based on the historical exposure status of the service and the total exposure quota.

[0091] The total exposure limit is the theoretical maximum exposure of the service corresponding to the labeled data during the application's operation, as predicted in the preceding steps based on display preferences. It reflects the quota limit for the service corresponding to the labeled data. The quota limits for different services may be the same or different.

[0092] If the tagged data for a service is exposed, then the service's quota will be consumed, meaning the remaining exposure quota will be reduced. Specifically, each time the tagged data achieves a valid exposure in the application, the corresponding quota unit will be deducted from the remaining exposure quota until the remaining exposure quota reaches zero. For example, as described above, after displaying the tagged data in the second tagging information, the method further includes updating the remaining exposure quota corresponding to the tagged data in the second tagging information. The advantage of this is that it allows for real-time tracking of the exposure consumption of each service, ensuring the dynamic accuracy of quota management.

[0093] The real-time update of the remaining exposure quota needs to be synchronized to the tagging server to ensure that display decisions are made accurately based on the remaining exposure quota.

[0094] This implementation method allows for the determination of display preferences based on the target account's characteristics. This enables applications to gain deep insights into the differentiated display needs of various accounts for tagged data across different services, achieving personalized exposure quota configurations. By predicting the total exposure quota using display preferences, a shift from "average allocation" to "on-demand configuration" is achieved. Accounts with high preferences receive more exposure quota support, while accounts with low preferences receive a corresponding reduction in quota, minimizing disruption and thus optimizing the overall display effect of tagged data. Combining the existing exposure status with the total exposure quota to calculate the remaining exposure quota forms a complete closed-loop quota management mechanism. This prevents excessive exposure of high-frequency services from interfering with users while ensuring that low-frequency but high-value services receive the necessary exposure opportunities, effectively balancing the dual goals of user experience and business promotion. Furthermore, this technical solution enhances adaptability. When account characteristics change, the display preference dynamically adjusts, leading to a linked update of the total exposure quota and remaining exposure quota. Therefore, it can continuously respond to the evolution of account needs, improving the timeliness and relevance of tagged data display.

[0095] S520. Determine the target marker data that meets the exposure requirements with the remaining exposure allowance.

[0096] If the remaining exposure quota is 0, it means that the labeled data has used up its quota and can no longer be exposed. Therefore, labeled data with a remaining exposure quota greater than 0 can be used as target labeled data.

[0097] S530. Based on the display recommendation degree corresponding to the target tag data, determine the second tag information to be displayed.

[0098] In this implementation, real-time calculation of remaining exposure quotas avoids overexposure of single data or imbalanced resource allocation, ensuring the rationality of exposure opportunities. Introducing display recommendation as a secondary screening indicator comprehensively considers multiple factors of the labeled data, prioritizing limited exposure resources for target labeled data with higher overall value, thereby improving the overall display effect. This satisfies the business's promotional needs for labeled data while also taking into account the personalized experience of end users, optimizing the display effect while controlling the exposure pace.

[0099] In one exemplary implementation, determining the second tag information to be displayed based on the display recommendation score corresponding to the target tag data includes: S531. For each of the target labeled data, a corresponding display recommendation score is determined based on the target labeled data interaction rate and the target labeled data interaction quality, wherein the target labeled data interaction rate indicates the target account's labeled data interaction rate with the service corresponding to the target labeled data, and the labeled data interaction quality indicates the target account's labeled data interaction quality with the service corresponding to the target labeled data.

[0100] The target tagged data interaction rate and interaction quality can each indicate the consumption rate and quality of the target tagged data. The target tagged data interaction rate reflects the target account's activity level in consuming tagged data for this service, which can be quantified by the frequency of interactions per unit of time. The target tagged data interaction quality characterizes the effective value and depth of the target account's interaction with the service's tagged data, focusing on the actual business benefits brought by the interaction rather than simply the accumulation of quantity. This can be determined through post-interaction interactions, such as the duration of stay on service-related pages and the frequency of interaction on those pages.

[0101] In one exemplary implementation, determining the corresponding display recommendation score based on the target tag data interaction rate and the target tag data interaction quality includes: S1. Obtain a target relationship graph, which indicates the interaction relationship between an account and tagged data. The horizontal axis of the target relationship graph represents the interaction rate of the account's tagged data, and the vertical axis represents the interaction quality of the account's tagged data.

[0102] Specifically, a target relationship diagram can be constructed for the business corresponding to the target labeled data. This target relationship diagram is determined based on the interaction performance of each account with the labeled data of that business.

[0103] For example, the target relationship graph can be constructed by: determining the average interaction rate based on the tag data interaction rate of each account; determining the average interaction quality based on the tag data interaction quality of each account; determining the center of the target relationship graph based on the average interaction rate and the average interaction quality; and generating the target relationship graph including four quadrants based on the center, the horizontal axis, and the vertical axis, each quadrant corresponding to a set of parameters used to calculate the display recommendation degree.

[0104] During the construction process, the central coordinates of the target relationship graph are jointly determined by the aforementioned average interaction rate and average interaction quality. This central point divides the two-dimensional plane into four quadrants. The first quadrant consists of accounts whose interaction rate and quality for the labeled data of this business are both above average, exhibiting high-frequency and high-value interaction characteristics. The second quadrant consists of accounts whose interaction rate for the labeled data of this business is below average but whose interaction quality is above average, exhibiting a low-frequency but high-quality interaction pattern. The third quadrant consists of accounts whose corresponding indicators are both below average, exhibiting relatively passive interaction behavior. The fourth quadrant consists of accounts whose interaction rate for the labeled data of this business is above average but whose interaction quality is below average, exhibiting high-frequency but low-quality interaction characteristics.

[0105] The parameter sets differ for each quadrant. For Quadrant I accounts, the parameter set focuses on strengthening positive incentives for display and recommendation; for Quadrant II accounts, the parameter set may encourage positive incentives to some extent; for Quadrant III accounts, the parameter set minimizes positive incentives; and for Quadrant IV accounts, the parameter set may provide partial positive incentives.

[0106] This construction method avoids the one-sidedness of a single indicator and naturally supports differentiated incentives for displaying recommendation levels by proposing a four-quadrant structure. Using the average level of the group as the dividing benchmark ensures the dynamic adaptability of the incentives. When the characteristics of the user group change, the incentive method disclosed herein can still be applied, making it easy to operate and sustainable.

[0107] S2. Based on the target tag data interaction rate and the target tag data interaction quality, determine the position of the target account in the target relationship graph.

[0108] Using the target-labeled data interaction rate and target-labeled data interaction quality as the x and y axes respectively, the corresponding positions can be determined in this target relationship diagram.

[0109] S3. Based on the location, determine the corresponding display recommendation level.

[0110] In this implementation, a target relationship graph is constructed with the interaction rate of the tagged data on the horizontal axis and the interaction quality on the vertical axis. This transforms the originally isolated two-dimensional indicators into a visualized two-dimensional spatial distribution, allowing the interaction relationship between accounts and tagged data to be presented intuitively. This visualization modeling method overcomes the limitations of single-indicator evaluation, simultaneously considering both the timeliness and effectiveness of the interaction, avoiding reliance solely on interaction rate while neglecting quality, or focusing solely on quality while ignoring timeliness. Determining the display recommendation degree based on the specific position of the target account in the target relationship graph significantly improves the accuracy of predicting the display recommendation degree of the target tagged data.

[0111] In one exemplary implementation, determining the corresponding display recommendation score based on the location includes: determining the target quadrant in the target relationship graph where the location is located; and calculating the display recommendation score based on the target parameter set corresponding to the target quadrant.

[0112] Specifically, the target relationship graph is divided into high-interaction-rate and low-interaction-rate regions, using the center point as the boundary; similarly, it is divided into high-interaction-quality and low-interaction-quality regions, also using the center point as the boundary. This results in four quadrants for the entire target relationship graph: the first quadrant represents both high-interaction-rate and high-interaction-quality regions, the second quadrant represents both low-interaction-rate and high-interaction-quality regions, the third quadrant represents both low-interaction-rate and low-interaction-quality regions, and the fourth quadrant represents both high-interaction-rate and low-interaction-quality regions. Each quadrant corresponds to a parameter set. Based on the target account's location within the target quadrant of the target relationship graph, the target parameter set corresponding to the target account can be obtained, thereby determining the parameter set for calculating the display recommendation score of the target tag data tailored to the target account.

[0113] In this implementation, quadrant division allows for clearer categorization of interaction features, ensuring that different interaction features correspond to different parameter sets. The display recommendation score is calculated based on the target parameter set corresponding to the target quadrant, achieving targeted display recommendation score calculations based on the interaction features of the target account. This ultimately results in a personalized display effect for the target-tagged data.

[0114] In one exemplary implementation, the target parameter set includes a first parameter, a second parameter, and a third parameter. Calculating the display recommendation score based on the target parameter set corresponding to the target quadrant includes: S10. Based on the target account's interaction with the business corresponding to the target tag data and the first parameter, determine the first recommendation component.

[0115] This interaction data refers to statistical information on various interactive behaviors of the target account over a past period regarding historical tagged data within the same business domain as the "target tagged data" for which the current display recommendation score is to be calculated. These interactive behaviors may include, but are not limited to, clicking to view, saving, commenting, sharing, downloading, and prolonged viewing. By quantitatively analyzing these interactive behaviors, such as statistically analyzing the frequency, depth, duration, and diversity of interaction types, a comprehensive indicator reflecting the target account's preference for the tagged data of that business and the level of interactive activity can be formed. Then, this comprehensive indicator is combined with the "first parameter" in the target parameter set and calculated using a preset algorithm (such as multiplication or more complex functional relationships). The final result is the "first recommendation score component." This component is mainly used to measure the target account's potential interest and acceptance of the current target tagged data based on historical interaction habits and is an important component of the final display recommendation score.

[0116] S20. Based on the first page position of the currently displayed page in the second time period, the second page position of the business corresponding to the target tag data, and the second parameter, determine the second recommendation component.

[0117] Here, "first page position" refers to the specific location of the page currently displayed to the user within the overall application's business page system, such as whether it is the homepage, list page, details page, or a specific functional module page. "Second page position" refers to the location of the business function to which the target data belongs within the overall business page system. "Second parameter" is a parameter used to quantify the impact of page position on recommendations.

[0118] The relationship between the two page locations is analyzed, including whether they are identical, whether there is a hierarchical inclusion relationship, and the degree of correlation within the user's browsing path. Then, combined with a second parameter, this location information is processed using a pre-defined algorithm (e.g., if the first and second page locations are highly related or overlapped, the second recommendation component may take a higher value; if the correlation is low or they are far apart, the component value may be lower) to obtain the second recommendation component. This component primarily measures the suitability and relevance of the target labeled data in the current user's browsing page location, serving as an important supplement to the recommendation score from the perspective of page spatial location.

[0119] S30. Based on the remaining exposure quota corresponding to the target marker data and the third parameter, determine the third recommendation component.

[0120] The "remaining exposure quota" here reflects the potential and space for promotion and display of the target data. Through the role of the third parameter, it is transformed into a recommendation score component that reflects the urgency and possibility of promotion. Specifically, if the remaining exposure quota of the target data is high, it means that there are still many unused display opportunities in the current period. In this case, combined with an appropriate third parameter, the third recommendation score component may take a higher value to prioritize the recommendation of these data tags that still have a large promotion space. Conversely, if the remaining exposure quota is low, close to or already reached the preset limit, the third recommendation score component may also be low, because there is not much room for further exposure.

[0121] S40. Calculate the display recommendation score based on the first recommendation score component, the second recommendation score component, and the third recommendation score component.

[0122] In this step, the three components can be added together to form a final display recommendation score. This score comprehensively reflects the overall performance of the target data across multiple dimensions, including user interest matching, historical interaction effects, efficiency of promotional resource utilization, and achievement of exposure goals. It allows for a more comprehensive and objective evaluation of the promotional value of each target data point, thereby determining a reasonable display recommendation score.

[0123] S532. Sort the target labeled data in descending order of recommendation level to obtain a sorted list.

[0124] S533. Based on the sorted list, determine the second tag information.

[0125] For example, determining the second tag information based on the sorted list includes: obtaining the tag data display quota K corresponding to the page currently being displayed in the second time period; and selecting the first K tag data in the sorted list to obtain the second tag information.

[0126] The determination of the K value can take into account multiple factors, such as the actual displayable area of ​​the current page on the client, the average display space occupied by a single tag, and the user's past browsing habits of the tag data on that page (such as average dwell time, scrolling frequency, etc.). For example, if a recommendation page on the client can clearly display a maximum of 8 tags at a time, then the K value may be set to 8.

[0127] Taking a specific scenario as an example, suppose the homepage of a news and information client, after page layout analysis and user experience testing, determines that the optimal number of tagged data items displayed at one time is 5 (i.e., K=5). When the tagging server calculates a sorted list of target tagged data through the aforementioned steps, for example, the sorted result is [A, B, C, D, E, F, G, ...] (arranged from high to low in terms of display recommendation), then the server will select the first 5 tagged data items A, B, C, D, and E from this sorted list to form the second tagging information and push it to the client.

[0128] Determining the second tag information in this way effectively avoids problems such as slow page loading and user information overload, improving client-side efficiency and user experience. Secondly, selecting the top K tags based on the sorted list ensures that the tags with the highest recommendation rate and best overall value are prioritized for user viewing, maximizing the exposure efficiency of high-quality tags and thus increasing the probability of user interaction with the tags, enhancing the accuracy and effectiveness of the displayed tags. The tagging server can dynamically adjust the K value according to the characteristics of different client pages, achieving flexibility and adaptability in display decisions, better meeting diverse display needs and scenario-based application requirements. Determining the display quota through quantitative methods and combining it with the sorting results ensures that the entire process is clear, interpretable, and avoids interference from subjective factors, guaranteeing the objectivity and fairness of the displayed tags.

[0129] In the embodiments of this disclosure, by introducing two dimensions—the interaction rate and the interaction quality of target-tagged data—to predict the display recommendation degree, a refined and quantitative evaluation of the display value of target-tagged data can be achieved. The interaction rate of target-tagged data reflects the responsiveness of the target account to the service, reflecting the timeliness and attractiveness of the service and the immediate attention of users; the interaction quality of target-tagged data reveals the depth and effectiveness of the interaction, characterizing the degree to which the service content matches the true value of the target account. By organically combining the two, the display recommendation degree considers both the frequency characteristics of the interaction and the substantive effectiveness of the interaction, thus providing a more comprehensive and objective assessment.

[0130] Sort the target tag data in descending order of display recommendation value. This ensures that the top of the sorted list displays the tag data with the best overall value. This allows limited display resources to be prioritized for the tag data with the highest display value, thereby improving end-user experience satisfaction and maximizing the promotion efficiency of the tag server.

[0131] In one exemplary implementation, please refer to Figure 6 This is a schematic diagram illustrating the process of making decisions on displaying labeled data on the labeling server side in this disclosure.

[0132] In this process, the total amount of tag data corresponding to each business can be calculated based on the account characteristics of the target account. Then, the amount consumed by the tag data that has already been displayed for that business is subtracted to obtain the remaining amount of tag data for that business. If the remaining amount is not greater than 0, the corresponding tag cannot be displayed and is discarded.

[0133] For target-labeled data that has not been discarded, a target relationship graph corresponding to that business is obtained. This graph describes the relationship between the interaction speed and quality of the labeled data for each account within that business. Based on the interaction speed and quality of the target account's target-labeled data for that business, the target quadrant of the target account within the target relationship graph is determined, thus obtaining the target parameter set corresponding to the target quadrant. Based on this target parameter set... Figure 6 The formula shown calculates the recommendation score for the target labeled data.

[0134] The target parameter set may include Figure 6 In / / Then, the recommendation score can be calculated using the formula. The meanings of the other parameters in this formula are as follows: v1 = Historical click-through rate of user-tagged data for this service, v2 = Service priority of this service, v3 = User activity level for this service, Q: Global adjustment coefficient.

[0135] Figure 7 This is a block diagram illustrating a tag data processing apparatus according to an exemplary embodiment. (Refer to...) Figure 7 The device includes: The tag data receiving module 710 is used to receive first tag information in a first time period, the first tag information including at least one tag data, the tag data being used to indicate the service status of the corresponding service; The tag data processing module 720 is used to send the first tag information to the tag server in the second time period, so that the tag server makes a display decision on the tag data in the first tag information and obtains the second tag information to be displayed, wherein the second tag information includes at least one tag data. The tag data display module 730 is used to display the tag data in the second tag information on the page currently being displayed during the second time period.

[0136] In one exemplary embodiment, the tag data receiving module 710 is configured to: Send a tag information retrieval request to the tagging server so that the tagging server sends a tag data retrieval request to the service provider corresponding to each service, makes a display decision on the tag data returned by each service provider, generates and returns third tag information, the third tag information being part of the first tag information; or, Receive tagging data pushed by any of the service providers, wherein the tagging data belongs to the first tagging information.

[0137] In one exemplary embodiment, the tag data processing module 720 is configured to: Determine the remaining exposure quota corresponding to each of the marker data in the first marker information; Determine the target marker data that meets the exposure requirements with the remaining exposure allowance; Based on the display recommendation rate corresponding to the target tag data, the second tag information to be displayed is determined.

[0138] In one exemplary embodiment, the tag data processing module 720 is configured to: Based on the account characteristics of the target account, the display preference degree corresponding to the tagged data is determined. The display preference degree is used to indicate the degree of display preference of the target account for the prompt information of the business to which the tagged data belongs. The target account is the account used to display the tagged data. Based on the display preference, predict the total exposure amount corresponding to the labeled data; Based on the historical exposure data of the service and the total exposure amount, the remaining exposure amount of the labeled data is determined.

[0139] In one exemplary implementation, the account characteristics of the target account include at least one of the following: The target account's static attribute characteristics, the target account's device characteristics, and the target account's interactive behavior characteristics.

[0140] In one exemplary embodiment, the tag data processing module 720 is configured to: For each target tagged data, a corresponding display recommendation score is determined based on the target tagged data interaction rate and the target tagged data interaction quality. The target tagged data interaction rate indicates the target account's tagged data interaction rate with the business corresponding to the target tagged data, and the tagged data interaction quality indicates the target account's tagged data interaction quality with the business corresponding to the target tagged data. The target-labeled data is sorted in descending order of recommendation level to obtain a sorted list; The second tagging information is determined based on the sorted list.

[0141] In one exemplary embodiment, the tag data processing module 720 is configured to: Obtain a target relationship graph, which indicates the interaction relationship between an account and tagged data. The horizontal axis of the target relationship graph represents the rate of interaction of the account with tagged data, and the vertical axis represents the quality of interaction of the account with tagged data. Based on the target tag data interaction rate and the target tag data interaction quality, the position of the target account in the target relationship graph is determined; Based on the location, the corresponding display recommendation level is determined.

[0142] In one exemplary embodiment, the tag data processing module 720 is configured to: The average interaction rate is determined based on the interaction rate of the marked data for each of the aforementioned accounts; The average interaction quality is determined based on the interaction quality of the tagged data for each account; The center of the target relationship graph is determined based on the average interaction rate and the average interaction quality. Based on the center, the horizontal axis, and the vertical axis, a target relationship graph comprising four quadrants is generated. Each quadrant corresponds to a set of parameters, which are used to calculate the display recommendation degree.

[0143] In one exemplary embodiment, the tag data processing module 720 is configured to: Determine the target quadrant in the target relationship diagram where the location is situated; The display recommendation score is calculated based on the target parameter set corresponding to the target quadrant.

[0144] In one exemplary embodiment, the target parameter set includes a first parameter, a second parameter, and a third parameter, and the marker data processing module 720 is used for: Based on the target account's interaction with the business corresponding to the target tag data, and the first parameter, a first recommendation component is determined; Based on the first page position of the currently displayed page in the second time period, the second page position of the business corresponding to the target tag data, and the second parameter, a second recommendation component is determined; Based on the remaining exposure quota corresponding to the target marker data and the third parameter, the third recommendation component is determined; The display recommendation score is calculated based on the first recommendation score component, the second recommendation score component, and the third recommendation score component.

[0145] In one exemplary embodiment, the tag data processing module 720 is configured to: Obtain the tag data display quota K corresponding to the page currently being displayed in the second time period; In the sorted list, the first K labeled data are selected to obtain the second labeled information.

[0146] In one exemplary embodiment, the tag data processing module 720 is configured to: Update the remaining exposure quota corresponding to the tag data in the second tag information.

[0147] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0148] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided. Optionally, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform any of the methods described above.

[0149] In an exemplary embodiment, a computer program product is also provided, the computer program product including a computer program stored in a readable storage medium, wherein at least one processor of a computer device reads from the readable storage medium and executes the computer program, causing the device to perform any of the methods described above.

[0150] Figure 8 This is a block diagram illustrating an electronic device for tag data processing according to an exemplary embodiment. The electronic device may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the device may include an RF (Radio Frequency) circuit 810, a memory 820 including one or more computer-readable storage media, an input unit 830, a display unit 840, a sensor 850, an audio circuit 860, a WiFi (Wireless Fidelity) module 870, a processor 880 including one or more processing cores, and a power supply 890, among other components. Those skilled in the art will understand that... Figure 8 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The RF circuit 810 can be used for receiving and transmitting signals during information transmission or calls. Specifically, it receives downlink information from the base station and hands it over to one or more processors 880 for processing; additionally, it transmits uplink data to the base station. Typically, the RF circuit 810 includes, but is not limited to, an antenna, at least one amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, an LNA (Low Noise Amplifier), a duplexer, etc. Furthermore, the RF circuit 810 can also communicate wirelessly with networks and other terminals. Wireless communication can use any communication standard or protocol, including but not limited to GSM (Global System for Mobile communication), GPRS (General Packet Radio Service), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), LTE (Long Term Evolution), email, SMS (Short Messaging Service), etc.

[0151] The memory 820 can be used to store software programs and modules. The processor 880 executes various functional applications and data processing by running the software programs and modules stored in the memory 820. The memory 820 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, system requirements for functions, etc.; the data storage area may store data created based on the use of the terminal, etc. In addition, the memory 820 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. Accordingly, the memory 820 may also include a memory controller to provide access to the memory 820 for the processor 880 and the input unit 830.

[0152] The input unit 830 can be used to receive input digital or character information, and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control. Specifically, the input unit 830 may include a touch-sensitive surface 831 and other input devices 832. The touch-sensitive surface 831, also known as a touch display screen or touchpad, can collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch-sensitive surface 831), and drive the corresponding connected devices according to a pre-set program. Optionally, the touch-sensitive surface 831 may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and the signal generated by the touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 880, and can also receive and execute commands sent by the processor 880. In addition, the touch-sensitive surface 831 can be implemented using various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch-sensitive surface 831, the input unit 830 may also include other input devices 832. Specifically, other input devices 832 may include, but are not limited to, one or more of the following: physical keyboard, function keys (such as volume control buttons, power buttons, etc.), trackball, mouse, joystick, etc. The display unit 840 can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the terminal. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. The display unit 840 may include a display panel 841, which may optionally be configured as an LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or similar display panel 841. Further, a touch-sensitive surface 831 may cover the display panel 841. When the touch-sensitive surface 831 detects a touch operation on or near it, it transmits the information to the processor 880 to determine the type of touch event. Subsequently, the processor 880 provides corresponding visual output on the display panel 841 according to the type of touch event. The touch-sensitive surface 831 and the display panel 841 can be two independent components to implement input and output functions. However, in some embodiments, the touch-sensitive surface 831 and the display panel 841 can be integrated to achieve input and output functions.

[0153] The terminal may also include at least one sensor 850, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display panel 841 according to the ambient light level, and the proximity sensor can turn off the display panel 841 and / or the backlight when the terminal is moved to the ear. As a type of motion sensor, a gravity acceleration sensor can detect the magnitude of acceleration in various directions (generally three axes). When stationary, it can detect the magnitude and direction of gravity and can be used for applications that identify the terminal's posture (such as landscape / portrait switching, related games, magnetometer posture calibration), vibration recognition-related functions (such as pedometer, tapping), etc. Other sensors that may be configured on the terminal, such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, will not be described in detail here.

[0154] Audio circuitry 860, speaker 861, and microphone 862 provide an audio interface between the user and the terminal. Audio circuitry 860 converts received audio data into electrical signals, which are then transmitted to speaker 861, where they are converted into sound signals for output. Conversely, microphone 862 collects sound signals, converts them into electrical signals, which are then received by audio circuitry 860, converted back into audio data, processed by processor 880, and transmitted via RF circuitry 810 to, for example, another terminal, or output to memory 820 for further processing. Audio circuitry 860 may also include an earphone jack to facilitate communication between a peripheral headset and the terminal.

[0155] WiFi is a short-range wireless transmission technology. This terminal, through the WiFi module 870, can help users send and receive emails, browse web pages, and access streaming media, providing users with wireless broadband internet access. Although Figure 8 WiFi module 870 is shown, but it is understood that it is not a necessary component of the terminal and can be omitted as needed without changing the nature of the invention.

[0156] The processor 880 is the control center of the terminal, connecting various parts of the terminal through various interfaces and lines. It executes software programs and / or modules stored in the memory 820, and calls data stored in the memory 820 to perform various functions and process data, thereby providing overall monitoring of the terminal. Optionally, the processor 880 may include one or more processing cores; preferably, the processor 880 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interaction area, and system, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 880.

[0157] The terminal also includes a power supply 890 (such as a battery) to power various components. Preferably, the power supply can be logically connected to the processor 880 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 890 may also include one or more DC or AC power supplies, a recharging system, a power fault detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0158] Although not shown, the terminal may also include a camera, Bluetooth module, etc., which will not be described in detail here. Specifically, in this embodiment, the display unit of the terminal is a touch screen display, and the terminal also includes a memory and one or more programs, wherein one or more programs are stored in the memory and configured to be executed by one or more processors of the instructions in the method embodiment of the present invention.

[0159] Please refer to Figure 9 This illustration shows another block diagram of an electronic device for tag data processing, provided by another exemplary embodiment of this disclosure. The computer device may be a server for performing the tag data processing method described above. Specifically: Computer device 900 includes a Central Processing Unit (CPU) 901, a system memory 904 including Random Access Memory (RAM) 902 and Read Only Memory (ROM) 903, and a system bus 905 connecting the system memory 904 and the CPU 901. Computer device 900 also includes a basic input / output system (I / O system) 906 that facilitates information transfer between various devices within the computer, and a mass storage device 907 for storing the operating system 913, system 914, and other program modules 911.

[0160] The basic input / output system 906 includes a display 908 for displaying information and an input device 909 for user input, such as a mouse or keyboard. Both the display 908 and the input device 909 are connected to the central processing unit 901 via an input / output controller 190 connected to the system bus 905. The basic input / output system 906 may also include the input / output controller 190 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 190 also provides output to a display screen, printer, or other types of output devices.

[0161] Mass storage device 907 is connected to central processing unit 901 via a mass storage controller (not shown) connected to system bus 905. Mass storage device 907 and its associated computer-readable media provide non-volatile storage for computer device 900. That is, mass storage device 907 may include computer-readable media (not shown) such as hard disk or CD-ROM (CompactDisc Read-Only Memory) drive.

[0162] Without loss of generality, computer-readable media can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes RAM, ROM, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash memory or other solid-state storage technologies, CD-ROM, DVD (Digital Video Disc) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that computer storage media are not limited to the above-mentioned types. The system memory 904 and mass storage device 907 described above can be collectively referred to as memory.

[0163] According to various embodiments of this disclosure, the computer device 900 can also be connected to a remote computer on a network, such as the Internet. That is, the computer device 900 can be connected to a network 912 via a network interface unit 911 connected to a system bus 905, or the network interface unit 911 can be used to connect to other types of networks or remote computer systems (not shown).

[0164] The aforementioned memory also includes a computer program stored in the memory and configured to be executed by one or more processors to implement the aforementioned tag data processing method.

[0165] In an exemplary embodiment, a computer-readable storage medium is also provided, which stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is executed by a processor to implement the above-described marker data processing method.

[0166] Optionally, the computer-readable storage medium may include: ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drives), or optical disc, etc. The random access memory may include ReRAM (Resistance Random Access Memory) and DRAM (Dynamic Random Access Memory).

[0167] In an exemplary embodiment, a computer-readable storage medium including program code is also provided, such as a memory including program code, which can be executed by a processor to complete the above-described tag data processing method. Optionally, the computer-readable storage medium may be read-only memory (ROM), random access memory (RAM), compact-disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0168] In an exemplary embodiment, a computer program product is also provided, including a computer program that, when executed by a processor, implements the above-described tag data processing method.

[0169] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0170] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for processing labeled data, characterized in that, The method includes: In the first time period, first tagging information is received, the first tagging information including at least one tagging data, the tagging data being used to indicate the service status of the corresponding service; In the second time period, the first tagging information is sent to the tagging server so that the tagging server makes a display decision on the tagging data in the first tagging information and obtains the second tagging information to be displayed, the second tagging information including at least one tagging data; In the currently displayed page during the second time period, the tag data from the second tag information is displayed.

2. The labeled data processing method according to claim 1, characterized in that, The method further includes: Send a tag information retrieval request to the tag server, so that the tag server sends a tag data retrieval request to the service provider corresponding to each service, makes a display decision on the tag data fed back by each service provider, generates and feeds back third tag information, the third tag information being part of the first tag information; or, Receive tagging data pushed by any of the service providers, wherein the tagging data belongs to the first tagging information.

3. A method for processing labeled data according to claim 1 or 2, characterized in that, The labeling server makes display decisions using the following method: Determine the remaining exposure quota corresponding to each of the marker data in the first marker information; Determine the target marker data that meets the exposure requirements with the remaining exposure allowance; Based on the display recommendation rate corresponding to the target tag data, the second tag information to be displayed is determined.

4. The labeled data processing method according to claim 3, characterized in that, Determining the remaining exposure quota corresponding to each of the marked data in the first marking information includes performing the following operations for each of the marked data: Based on the account characteristics of the target account, the display preference degree corresponding to the tagged data is determined. The display preference degree is used to indicate the degree of display preference of the target account for the prompt information of the business to which the tagged data belongs. The target account is the account used to display the tagged data. Based on the display preference, predict the total exposure amount corresponding to the labeled data; Based on the historical exposure data of the service and the total exposure amount, the remaining exposure amount of the labeled data is determined.

5. The labeled data processing method according to claim 4, characterized in that, The target account's account characteristics include at least one of the following: The target account's static attribute characteristics, the target account's device characteristics, and the target account's interactive behavior characteristics.

6. The labeled data processing method according to claim 3, characterized in that, The step of determining the second tag information to be displayed based on the display recommendation score corresponding to the target tag data includes: For each target tagged data, a corresponding display recommendation score is determined based on the target tagged data interaction rate and the target tagged data interaction quality. The target tagged data interaction rate indicates the target account's tagged data interaction rate with the business corresponding to the target tagged data, and the tagged data interaction quality indicates the target account's tagged data interaction quality with the business corresponding to the target tagged data. The target-labeled data is sorted in descending order of recommendation level to obtain a sorted list; The second tagging information is determined based on the sorted list.

7. The labeled data processing method according to claim 6, characterized in that, The determination of the corresponding display recommendation degree based on the target tag data interaction rate and target tag data interaction quality includes: Obtain a target relationship graph, which indicates the interaction relationship between an account and tagged data. The horizontal axis of the target relationship graph represents the rate of interaction of the account with tagged data, and the vertical axis represents the quality of interaction of the account with tagged data. Based on the target tag data interaction rate and the target tag data interaction quality, the position of the target account in the target relationship graph is determined; Based on the location, the corresponding display recommendation level is determined.

8. The labeled data processing method according to claim 7, characterized in that, Before obtaining the target relationship graph, the method further includes: The average interaction rate is determined based on the interaction rate of the marked data for each of the aforementioned accounts; The average interaction quality is determined based on the interaction quality of the tagged data for each account; The center of the target relationship graph is determined based on the average interaction rate and the average interaction quality. Based on the center, the horizontal axis, and the vertical axis, a target relationship graph comprising four quadrants is generated. Each quadrant corresponds to a set of parameters, which are used to calculate the display recommendation degree.

9. The labeled data processing method according to claim 8, characterized in that, The determination of the corresponding display recommendation level based on the location includes: Determine the target quadrant in the target relationship diagram where the location is situated; The display recommendation score is calculated based on the target parameter set corresponding to the target quadrant.

10. A method for processing labeled data according to claim 9, characterized in that, The target parameter set includes a first parameter, a second parameter, and a third parameter. The calculation of the display recommendation degree based on the target parameter set corresponding to the target quadrant includes: Based on the target account's interaction with the business corresponding to the target tag data, and the first parameter, a first recommendation component is determined; Based on the first page position of the currently displayed page in the second time period, the second page position of the business corresponding to the target tag data, and the second parameter, a second recommendation component is determined; Based on the remaining exposure quota corresponding to the target marker data and the third parameter, the third recommendation component is determined; The display recommendation score is calculated based on the first recommendation score component, the second recommendation score component, and the third recommendation score component.

11. The labeled data processing method according to claim 6, characterized in that, Determining the second tag information based on the sorted list includes: Obtain the tag data display quota K corresponding to the page currently being displayed in the second time period; In the sorted list, the first K labeled data are selected to obtain the second labeled information.

12. The labeled data processing method according to claim 3, characterized in that, After displaying the tag data in the second tag information, the method further includes: Update the remaining exposure quota corresponding to the tag data in the second tag information.

13. A tag data processing apparatus, characterized in that, The device includes: The tag data receiving module is used to receive first tag information in a first time period, the first tag information including at least one tag data, the tag data being used to indicate the service status of the corresponding service; The tag data processing module is used to send the first tag information to the tag server in the second time period, so that the tag server makes a display decision on the tag data in the first tag information and obtains the second tag information to be displayed, wherein the second tag information includes at least one tag data. The tag data display module is used to display the tag data in the second tag information on the page currently being displayed during the second time period.

14. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the tag data processing method as described in any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is enabled to perform the tag data processing method as described in any one of claims 1 to 12.

16. A computer program product, characterized in that, The computer program product includes a computer program stored in a readable storage medium, wherein at least one processor of a computer device reads from and executes the computer program, causing the device to perform the tag data processing method as described in any one of claims 1 to 12.

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