Data processing method and device, equipment, storage medium and product

Abstract

The embodiment of the invention discloses a data processing method and device, equipment, a storage medium and a product. The method comprises the steps that in response to the situation that personalized recommendation content meets a trigger condition, a rendering frame is called to obtain data of the personalized recommendation content from a server developed by adopting a native language, the rendering frame is a cross-end development frame, the data of the personalized recommendation content is subjected to adaptation processing through the rendering frame, and the data subjected to adaptation processing are obtained; and rendering the data subjected to the adaptive processing through a rendering framework to obtain a rendering result of the personalized recommendation content, and displaying the personalized recommendation content in a user page according to the rendering result of the personalized recommendation content. Therefore, after the rendering framework and the server are layered, the cross-end development framework is adopted as the rendering framework, and the server is developed through the native language, so that the rendering efficiency can be ensured while cross-end consistency rendering is realized.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a data processing method, a data processing apparatus, a computer device, a computer-readable storage medium, and a data processing product. Background Technology

[0002] With advancements in technology, the internet has become an indispensable part of people's daily lives. People can browse all sorts of information online. To ensure that recommended information better matches user preferences, personalized recommendations are commonly used. Research has found that due to differences in the devices used by different users and the varying recommended content for each user, achieving consistent rendering across different platforms while maintaining rendering efficiency has become a hot research topic. Summary of the Invention

[0003] This application provides a data processing method, apparatus, device, computer-readable storage medium, and product that can achieve cross-platform consistent rendering while ensuring rendering efficiency.

[0004] On one hand, embodiments of this application provide a data processing method, including:

[0005] When the personalized recommendation content meets the triggering conditions, the rendering framework is called to retrieve the personalized recommendation content data from the server. The server is developed using a native language, while the rendering framework is a cross-platform development framework.

[0006] The rendering framework is used to adapt the data of personalized recommendation content to obtain adapted data that meets the data adaptation requirements of the components associated with the user page.

[0007] The rendering framework is used to render the adapted data to obtain the rendering result of personalized recommendation content.

[0008] Based on the rendering results of the personalized recommendations, the personalized recommendations are displayed on the user's page.

[0009] On one hand, embodiments of this application provide a data processing apparatus, which includes:

[0010] The acquisition unit is used to respond to the personalized recommendation content meeting the triggering conditions by calling the rendering framework to retrieve the personalized recommendation content data from the server. The server is developed using a native language, while the rendering framework is a cross-platform development framework.

[0011] The processing unit is used to adapt the data of personalized recommendation content through the rendering framework to obtain adapted data that meets the data adaptation requirements of the components associated with the user page.

[0012] And it is used to render the adapted data through a rendering framework to obtain the rendering results of personalized recommendation content;

[0013] The display unit is used to display personalized recommended content on the user's page according to the rendering results of the personalized recommended content.

[0014] In one embodiment, the processing unit is further configured to:

[0015] When the data of the personalized recommendation content is detected to meet the preloading conditions, the personalized recommendation content data is preloaded by the server to obtain the personalized recommendation content data;

[0016] The preloading conditions include: the probability of personalized recommendation content being triggered is greater than a probability threshold.

[0017] In one implementation, the processing unit is configured to, when it detects that the data of the personalized recommendation content meets the preloading conditions, preload the data of the personalized recommendation content through the server to obtain the personalized recommendation content data, specifically configured to:

[0018] Retrieve the recommendation message stream, which includes M identifiers of personalized recommendation content. The identifier of any personalized recommendation content is used to retrieve the data of that personalized recommendation content from the server. M is a positive integer.

[0019] If the identifier of at least one target personalized recommendation content is not included in the exposure progress identifier, the server will preload the data of at least one target personalized recommendation content to obtain the data of at least one target personalized recommendation content; the exposure progress identifier records the identifiers of the personalized recommendation content that have been exposed.

[0020] In one implementation, the processing unit is configured to preload data of at least one target personalized recommendation content from the server to obtain data of at least one target personalized recommendation content, specifically for:

[0021] Trigger the preloading of at least one target personalized recommendation content on the server side;

[0022] Based on the update status of the recommendation message stream, determine whether at least one target personalized recommendation content meets the preloading execution conditions;

[0023] When at least one target personalized recommendation content meets the preloading execution conditions, the server preloads the data of at least one target personalized recommendation content to obtain the data of at least one target personalized recommendation content.

[0024] In one implementation, the processing unit is configured to determine, based on the update status of the recommendation message stream, whether at least one target personalized recommendation content meets the preloading execution conditions, specifically configured to:

[0025] If the recommendation message stream is updated, then at least one target personalized recommendation content is determined to meet the preloading execution conditions;

[0026] If the recommendation message stream is not updated and its duration is greater than the duration threshold, then at least one target personalized recommendation content is determined to meet the preloading execution conditions.

[0027] If the recommended message stream is not updated and its duration is less than or equal to the duration threshold, then at least one target personalized recommendation content does not meet the preloading execution conditions.

[0028] In one implementation, the processing unit is used to adapt the personalized recommendation content data through a rendering framework to obtain adapted data, specifically for:

[0029] Construct at least one data unit based on the data from personalized recommendation content;

[0030] Based on the data adaptation requirements of the components associated with the user page, at least one data unit is transformed to obtain the adapted data.

[0031] In one implementation, the processing unit is used to render the adapted data through a rendering framework to obtain a rendering result of personalized recommendation content, specifically for:

[0032] The adapted data is bound to the components associated with the user page to obtain the components bound with the adapted data.

[0033] Render the components that are bound to the adapted data to obtain the rendering result of personalized recommendation content.

[0034] In one implementation, the processing unit is configured to, in response to personalized recommendation content meeting a triggering condition, invoke a rendering framework to obtain personalized recommendation content data from the server, specifically configured to:

[0035] When the personalized recommendation content meets the triggering conditions, the data service on the server is called through the language interaction interface so that the server can obtain and return the personalized recommendation content data through lightweight computing.

[0036] Obtain personalized recommendation data returned by the server from the language interaction interface.

[0037] In one embodiment, the processing unit is further configured to:

[0038] In response to the rendering framework meeting the warm-up conditions, the rendering framework is loaded with an empty shell.

[0039] Among them, empty shell loading is used to load the rendering framework code into system memory.

[0040] In one implementation, the rendering framework runs on the client, and the client and server adopt a layered architecture; the rendering framework includes a data fetch layer, an adaptation layer, and a view layer.

[0041] The data retrieval layer is used to retrieve personalized recommendation content data from the server; the adaptation layer is used to adapt the personalized recommendation content data; and the view layer is used to render the adapted data.

[0042] Accordingly, this application provides a computer device comprising:

[0043] Memory, which stores computer programs;

[0044] A processor is used to load computer programs to implement the above data processing methods.

[0045] Accordingly, this application provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and executed by the above-described data processing method.

[0046] Accordingly, this application provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned data processing method.

[0047] In this embodiment, in response to personalized recommendation content meeting the triggering condition, the rendering framework is invoked to obtain personalized recommendation content data from a server developed using a native language. The rendering framework is a cross-platform development framework. It adapts the personalized recommendation content data to obtain adapted data, which is then rendered to obtain the rendering result of the personalized recommendation content. The personalized recommendation content is then displayed on the user's page according to this rendering result. Therefore, by layering the rendering framework and server, using a cross-platform development framework as the rendering framework, and developing the server using a native language, consistent cross-platform rendering can be achieved while maintaining rendering efficiency. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 A data processing scenario diagram provided in an embodiment of this application;

[0050] Figure 2 A flowchart illustrating a data processing method provided in an embodiment of this application;

[0051] Figure 3a This is a schematic diagram illustrating a data acquisition method provided in an embodiment of this application;

[0052] Figure 3b An architecture diagram of a client and server provided for embodiments of this application;

[0053] Figure 3c This is a cross-platform rendering comparison diagram provided in an embodiment of this application;

[0054] Figure 4 A flowchart illustrating another data processing method provided in this application embodiment;

[0055] Figure 5a A data processing flowchart combining a recommendation strategy is provided for an embodiment of this application;

[0056] Figure 5b A schematic diagram of an adaptation process provided in an embodiment of this application;

[0057] Figure 5c An architecture diagram of a rendering framework provided in an embodiment of this application;

[0058] Figure 6 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application;

[0059] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0061] This application relates to technologies related to system architecture and content recommendation. A brief introduction to these technologies is provided below:

[0062] Personalized content recommendations: Personalized content recommendations are usually presented as a feed and are characterized by "a thousand people, a thousand faces". Specifically, they can include, but are not limited to, industry news, product recommendations, service recommendations, and multimedia data recommendations.

[0063] Cross-platform development framework: A cross-platform development framework is a technical solution that uses the system's underlying rendering library for self-drawing. In this application, the rendering framework can specifically be a rendering framework with its own rendering engine, such as Flutter, React Native, or CEF. This application mainly uses Flutter as an example to introduce the data processing methods provided in this application.

[0064] Server-side: Possesses the ability to process data across platforms, used to perform calculations related to data in personalized recommendation content.

[0065] Based on the aforementioned technologies related to system architecture and content recommendation, this application provides a data processing scheme that can ensure rendering efficiency while achieving cross-platform consistent rendering. Figure 1 A data processing scenario diagram provided for an embodiment of this application, such as Figure 1 As shown, the data processing scenario provided in this application includes a terminal device 101 and a server 102. The data processing solution provided in this application can be executed by the terminal device 101. The terminal device may include, but is not limited to: smartphones (such as Android phones, iOS phones, etc.), tablet computers, portable personal computers, mobile internet devices (MIDs), smart voice interaction devices, smart home appliances, vehicle terminals, aircraft, wearable devices, etc., and this application embodiment does not limit this; the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms, and this application embodiment does not limit this.

[0066] It should be noted that, Figure 1The number of terminal devices and servers is for illustrative purposes only and does not constitute an actual limitation of this application. Terminal device 101 and server 102 can be connected via wired or wireless means, and this application does not impose any restrictions on this. This application adopts an architecture that separates the rendering framework from the server. The server can be deployed on terminal device 101 or server 102. When both the rendering framework and the server are deployed on terminal device 101, server 102 may not be included in the data processing scenario.

[0067] The general flow of the data processing solution provided in this application is as follows:

[0068] (1) In response to the personalized recommendation content meeting the triggering conditions, terminal device 101 calls the rendering framework to obtain the personalized recommendation content data from the server. The server is developed using native languages ​​(such as C++, Rust, Go, etc.), while the rendering framework is a cross-platform development framework (such as Flutter, React Native, CEF, etc.), which runs on the client. This can be understood as the client being developed using a cross-platform language. The client (rendering framework) and server adopt a layered architecture. Compared to a server developed using a cross-platform language, a server developed using native languages ​​can more fully utilize device hardware resources during computation, resulting in higher efficiency (better performance). A client developed using a cross-platform development framework has higher compatibility than a client developed using native languages, ensuring consistent rendering of personalized recommendation content across different operating systems and platforms. By layering the client and server, using a cross-platform development language for the client and a native language for the server combines their respective advantages, achieving consistent cross-platform rendering while maintaining rendering efficiency. Triggering conditions may include, but are not limited to: personalized recommendations being clicked, the current time matching the preset trigger time of personalized recommendations, and the background detecting a push notification for personalized recommendations. It is understandable that personalized recommendations will differ across different (user-used) terminal devices.

[0069] In one implementation, in response to personalized recommendations meeting a trigger condition, terminal device 101 invokes a data service on the server via a Foreign Function Interface (FFI) to enable the server to obtain and return the personalized recommendation data through lightweight computation. Then, terminal device 101 retrieves the personalized recommendation data returned by the server from the FFI.

[0070] (2) The terminal device 101 adapts the personalized recommendation content data through a rendering framework to obtain adapted data that meets the data adaptation requirements of the components associated with the user page. In different systems or platforms, the data requirements (such as data format and data type) of the components associated with the user page vary. In one implementation, the terminal device 101 constructs at least one data cell based on the personalized recommendation content data, and performs data transformation on each data cell according to the data adaptation requirements of the components associated with the user page to obtain adapted data.

[0071] (3) The terminal device 101 renders the adapted data using a rendering framework to obtain the rendering result of the personalized recommendation content, and displays the personalized recommendation content on the user page according to the rendering result of the personalized recommendation content. In one embodiment, the personalized recommendation content data obtained by the rendering framework from the server includes the rendering result that needs to be calculated during the rendering process; that is, the rendering framework does not involve calculations during the rendering process of the adapted data.

[0072] As can be seen from the above data processing scheme, during the data processing, the server is mainly used to perform calculations related to the personalized recommendation content data (such as calculating rendering results). The server does not need to consider the business logic when the personalized recommendation content data is rendered on different platforms. The rendering framework (in the client) is mainly used to bind the personalized recommendation content data with the components associated with the user page, and render the personalized recommendation content data based on the binding relationship, without performing calculations related to the personalized recommendation content data. In other words, the data processing scheme provided in this application delegates the rendering and calculation of personalized recommendation content to the rendering framework and the server respectively, giving full play to the cross-platform rendering advantages of the rendering framework and the computing advantages of the server, achieving consistent rendering across platforms while ensuring rendering efficiency.

[0073] In this embodiment, in response to personalized recommendation content meeting the triggering condition, the rendering framework is invoked to obtain personalized recommendation content data from a server developed using a native language. The rendering framework is a cross-platform development framework. It adapts the personalized recommendation content data to obtain adapted data, which is then rendered to obtain the rendering result of the personalized recommendation content. The personalized recommendation content is then displayed on the user's page according to this rendering result. Therefore, by layering the rendering framework and server, using a cross-platform development framework as the rendering framework, and developing the server using a native language, consistent cross-platform rendering can be achieved while maintaining rendering efficiency.

[0074] Based on the above data processing scheme, this application proposes a more detailed data processing method. The data processing method proposed in this application will be described in detail below with reference to the accompanying drawings.

[0075] Please see Figure 2 , Figure 2 A flowchart illustrating a data processing method provided in this application embodiment, which can be executed by a computer device; for example, by... Figure 1 The terminal device 101 shown is executing.

[0076] like Figure 2 As shown, the data processing method may include the following steps S201-S204:

[0077] S201. In response to the personalized recommendation content meeting the triggering conditions, the rendering framework is invoked to obtain the personalized recommendation content data from the server.

[0078] The rendering framework is a cross-platform development framework (such as Flutter, React Native, CEF, etc.). The rendering framework runs on the client side, which can be understood as the client being developed based on a cross-platform language and installed on a computer device. The server side is developed using a native language (such as C++, Rust, Go, etc.). The server and client adopt a layered architecture. The server can be installed on a computer device or on a server. The client (rendering framework) and server use a layered architecture. The triggering conditions can be dynamically adjusted according to the recommendation strategy; this application does not impose any restrictions on this. For example, the triggering condition could be a push instruction for personalized recommended content detected by the backend.

[0079] In one implementation, in response to personalized recommendations meeting trigger conditions, the computer device invokes a rendering framework (from the network, database, server, etc.) to load the business logic data of the personalized recommendations. If the business logic data of the personalized recommendations does not require calculation, the computer device can directly use the business logic data of the personalized recommendations as the data for subsequent steps. Conversely, if the business logic data of the personalized recommendations requires calculation, the computer device can invoke a data service on the server through a Foreign Function Interface (FFI) to enable the server to perform lightweight calculations on the business logic data of the personalized recommendations and return the calculation results (i.e., the data of the personalized recommendations). The computer device then obtains the data of the personalized recommendations returned by the server from the Foreign Function Interface.

[0080] Figure 3a This is a schematic diagram illustrating a data acquisition method provided in an embodiment of this application. For example... Figure 3aAs shown, in response to personalized recommendations meeting the trigger conditions, the rendering framework initiates a service request to the server through the language interaction interface. This service request asks the server to perform lightweight calculations on the business logic data of the personalized recommendations and return the results. The server responds to this request by calling the corresponding service (method) to perform the lightweight calculations on the business logic data of the personalized recommendations and obtain the results. The server then adds the calculation results to the response information and returns the response information to the rendering framework through the language interaction interface.

[0081] Figure 3b This is a client-server architecture diagram provided for an embodiment of this application. Figure 3b As shown, the client calls an Application Programming Interface (API), specifically a ServiceApi, to send the encoded personalized recommendation content's business logic data to the language interaction interface via a service channel. When the native language is C++, the service channel is a CppChannel. Upon receiving the encoded personalized recommendation content's business logic data, the language interaction interface invokes a method or data service on the server via a remote call (RpcCall). The server responds to this request by performing a lightweight calculation on the personalized recommendation content's business logic data and adding the result to the response information. The server then encodes and encapsulates the response information and returns it to the client via the language interaction interface. The client retrieves the response information from the service channel, decapsulates and decodes it, and finally obtains the personalized recommendation content data.

[0082] S202. Adapt the personalized recommendation content data using a rendering framework to obtain the adapted data.

[0083] In different systems or platforms, the data requirements (such as data format and data type) of components associated with a user page vary. In one implementation, a computer device constructs at least one data cell based on personalized recommendation content data, and performs data transformation (such as format conversion) on each data cell according to the data adaptation requirements of the components associated with the user page, obtaining adapted data. In one embodiment, the computer device can use a mapper to map each data cell to obtain adapted data.

[0084] Optionally, after constructing at least one data unit, the computer device can use these data units to construct a list of data units, and use a Transformer to add, delete, modify, or change the list of data units, or implement some list display effects; for example, dividing lines, collapsing, expanding, etc. Furthermore, after obtaining the adapted data, the computer device can also use a Decorator to further package and embellish the adapted data; for example, adding a hover state, adding rounded corners, etc.

[0085] S203. Render the adapted data using a rendering framework to obtain the rendering result of personalized recommendation content.

[0086] In one implementation, the computer device binds the adapted data to components associated with the user page, resulting in components bound with the adapted data. Specifically, this binding refers to attaching the adapted data to the corresponding attributes or states of the components. The computer device then renders the components bound with the adapted data to obtain the rendered personalized recommendation content.

[0087] In another implementation, the personalized recommendation content data obtained by the rendering framework from the server includes the rendering results that need to be calculated during the rendering process; that is, the rendering framework does not involve calculations during the rendering of the adapted data.

[0088] S204. Based on the rendering results of the personalized recommendations, display the personalized recommendations on the user's page.

[0089] Different clients have different page sizes and layouts. By using a rendering framework to adapt the data of personalized recommendations, we can ensure the consistency of personalized recommendations across different clients.

[0090] Figure 3c This is a schematic diagram illustrating a cross-platform rendering comparison provided in an embodiment of this application. For example... Figure 3c As shown, page 301 is the mobile client page, which includes a user identifier 3011, an information title 3012, and an information thumbnail 3013. Similarly, page 302 is the desktop page, which includes a user identifier 3021 corresponding to user identifier 3011, an information title 3022 corresponding to information title 3012, and an information thumbnail 3023 corresponding to information thumbnail 3013. It should be noted that the page elements in pages 301 and 302 are for illustrative purposes only and do not constitute an actual limitation of this application.

[0091] In this embodiment, in response to personalized recommendation content meeting the triggering condition, the rendering framework is invoked to obtain personalized recommendation content data from a server developed using a native language. The rendering framework is a cross-platform development framework. It adapts the personalized recommendation content data to obtain adapted data, which is then rendered to obtain the rendering result of the personalized recommendation content. The personalized recommendation content is then displayed on the user's page according to this rendering result. Therefore, by layering the rendering framework and server, using a cross-platform development framework as the rendering framework, and developing the server using a native language, consistent cross-platform rendering can be achieved while maintaining rendering efficiency.

[0092] Please see Figure 4 , Figure 4 A flowchart illustrating another data processing method provided in this application embodiment, which can be executed by a computer device; for example, by... Figure 1 The terminal device 101 shown executes this. (As...) Figure 4 As shown, the data processing method may include the following steps S401-S406:

[0093] S401, the rendering framework meets the preheating conditions, and the rendering framework is loaded with an empty shell.

[0094] Preheating conditions may include, but are not limited to, a probability threshold greater than the probability of detecting a user page (such as a news recommendation page) being triggered. Empty shell loading is used to load the rendering framework's code into system memory. It should be noted that empty shell loading does not load business logic. The probability of a user page being triggered can be predicted based on factors such as user click history.

[0095] In one implementation, the rendering framework includes a data fetch layer, an adaptation layer, and a view layer; wherein the data fetch layer is used to obtain personalized recommendation content data from the server; the adaptation layer is used to adapt the personalized recommendation content data; and the view layer is used to render the adapted data.

[0096] S402. When the data of the personalized recommendation content is detected to meet the preloading conditions, the personalized recommendation content data is preloaded by the server to obtain the personalized recommendation content data.

[0097] Preloading can be understood as the server performing calculations related to the personalized recommendation content (such as calculating rendering results) before the personalized recommendation content is triggered. Preloading conditions may include, but are not limited to: receiving a recommendation message stream from the backend, detecting the trigger entry point of the personalized recommendation content (such as thumbnail links, title links, etc.) entering the target area (such as the central area, the user's focus area, etc.) on the page.

[0098] In one implementation, the server can be deployed on a computer device along with the rendering framework. The computer device directly preloads the personalized recommendation content data through the server to obtain the personalized recommendation content data. In one embodiment, the computer device obtains a recommendation message stream, which includes M identifiers of personalized recommendation content. The identifier of any personalized recommendation content is used to obtain the data of that personalized recommendation content from the server, where M is a positive integer.

[0099] If at least one identifier of the target personalized recommendation content is not included in the exposure progress identifier, the computer device preloads the target personalized recommendation content data through the server to obtain the target personalized recommendation content data. The exposure progress identifier records the identifiers of the personalized recommendation content that have already been exposed. For example, the identifier of the personalized recommendation content can be represented as `remote_id`, and the exposure progress identifier is the global variable `REMOTEID`. When personalized recommendation content is exposed, the computer device updates the `remote_id` of the exposed personalized recommendation content into `REMOTEID`, thus the exposure progress of the personalized recommendation content can be determined through `REMOTEID`. Furthermore, the computer device can periodically detect the recommendation message stream. When it detects that the recommendation message stream contains unexposed personalized recommendation content (i.e., the target personalized recommendation content), the computer device preloads the target personalized recommendation content data through the server to obtain the target personalized recommendation content data.

[0100] In one implementation, the process of obtaining the target personalized recommendation content data by preloading the target personalized recommendation content data through the server includes: the computer device triggering the preloading of the target personalized recommendation content in the server.

[0101] After triggering the preloading of the target personalized recommendation content, the computer device can determine whether at least one target personalized recommendation content meets the preloading execution conditions based on the update status of the recommendation message stream. Specifically, if the recommendation message stream is updated, the computer device determines that the target personalized recommendation content meets the preloading execution conditions; if the recommendation message stream is not updated and its duration is greater than a duration threshold, the computer device determines that the target personalized recommendation content meets the preloading execution conditions; if the recommendation message stream is not updated and its duration is less than or equal to the duration threshold, the computer device determines that the target personalized recommendation content does not meet the preloading execution conditions. Optionally, after triggering the preloading of the target personalized recommendation content, the computer device can update the exposure progress indicator based on the identifier of the target personalized recommendation content to avoid duplicate loading of the target personalized recommendation content.

[0102] Furthermore, when the target personalized recommendation content meets the preloading execution conditions, the computer device preloads the data of the target personalized recommendation content through the server, thus obtaining the target personalized recommendation content data. As can be seen from the above, after triggering the preloading of the target personalized recommendation content, the computer device does not need to immediately preload the data of the target personalized recommendation content. Instead, it determines whether to preload the data of the target personalized recommendation content based on the update status of the recommendation message stream. Compared to blindly preloading, this saves unnecessary computing resource overhead.

[0103] In another implementation, if the server is not deployed on a computer device, when the data of the personalized recommendation content is detected to meet the preloading conditions, the computer device can send a preloading notification to the server so that the server can preload the data of the personalized recommendation content and obtain the personalized recommendation content data.

[0104] Figure 5a This document provides a data processing flowchart incorporating a recommendation strategy, as illustrated in an embodiment of this application. Figure 5a As shown, when the preloading detection conditions are met, the computer device detects whether the recommendation message stream contains personalized recommendation content whose identifier has not been updated to the exposure progress identifier. The preloading detection conditions may include, but are not limited to: detecting an update to the recommendation message stream, or detecting that a trigger entry for personalized recommendation content enters a target area of ​​the page. In one embodiment, the identifier of the personalized recommendation content is carried in the recommendation message stream in the form of a queue. The computer device can detect whether the identifier of the last personalized recommendation content in the queue (the remote_id of last_msg) is included in the exposure progress identifier. If the recommendation message stream does not contain personalized recommendation content whose identifier has not been updated to the exposure progress identifier, the detection ends. If the recommendation message stream contains target personalized recommendation content whose identifier has not been updated to the exposure progress identifier, preloading is triggered, and the identifier of the target personalized recommendation content is updated to the exposure progress identifier. Furthermore, the computer device can detect whether there are updates in the recommendation message stream (e.g., whether the last_msg in the recommendation message stream has changed). If an update is detected, the computer device performs preloading of the target personalized recommendation content. If no update is detected in the recommendation message stream, and the duration is less than a duration threshold, it continues to wait (or periodically performs detection and judgment). If no update is detected in the recommendation message stream, and the duration is greater than or equal to the duration threshold, the computer device performs preloading of the target personalized recommendation content. In addition, when the exposure progress indicator cleanup conditions are met, the computer device cleans up the exposure progress indicator. The exposure progress indicator cleanup conditions include, but are not limited to: detecting that the user has exited the recommendation page, or that the trigger entry point for the recommendation content has left the user's field of vision.

[0105] S403. In response to the personalized recommendation content meeting the triggering conditions, the data retrieval layer is called to obtain the personalized recommendation content data from the server.

[0106] The data fetch layer is primarily used to fetch and encapsulate data. The encapsulation process can include, but is not limited to, data structure transformation. Besides obtaining personalized recommendation content data from the server, the data fetch layer can also retrieve data from the network, databases, etc., that can be directly used for rendering without requiring additional rendering calculations.

[0107] For the server, when it receives a data fetch request, if personalized recommendation content data has already been preloaded, it can directly return the personalized recommendation content data as the response information to the data fetch layer. If personalized recommendation content data has not been loaded, it performs lightweight calculations on the relevant business data of the personalized recommendation content, obtains the calculation result, and returns the calculation result as the response information to the data fetch layer.

[0108] S404. Adapt the personalized recommendation content data through the adaptation layer to obtain the adapted data.

[0109] The adaptation layer can determine the required data based on the business logic of personalized recommendations and retrieve this data through the data fetch layer. Besides retrieving the data for personalized recommendations (the rendering result calculated by the server based on the business logic data provided by the rendering framework), it can also retrieve data that can be directly used for rendering without additional rendering calculations, based on the business logic (from the network, database, etc.). After retrieving all the data required by the business logic, the adaptation layer performs adaptation processing on this data to obtain the adapted data.

[0110] Figure 5b This is a schematic diagram illustrating an adaptation process provided in an embodiment of this application. Figure 5bAs shown, after obtaining the personalized recommendation content data, the Producer first generates data cells based on the business logic and data of the personalized recommendations. Multiple data cells can be further used to construct a data cell list. Next, the Transformer allows for adding, deleting, modifying, or changing the data cell list, or implementing list display effects such as dividers, collapse, and expand. Then, the Mapper maps each data cell to obtain the adapted data, enabling data binding to the view. Furthermore, the Decorator can be used to wrap and embellish the adapted data, for example, by adding a hover state or rounded corners. Under the unified data production and processing flow of the rendering framework, the code style of any independent interface is consistent. A common component library composed of Component business components ensures a consistent user experience for any list item. The management and refreshing of data state are centralized within the framework, guaranteeing data state consistency as well.

[0111] S405. Render the adapted data through the view layer to obtain the rendering result of personalized recommendation content.

[0112] Figure 5c This is an architecture diagram of a rendering framework provided in an embodiment of this application. Figure 5c As shown, the rendering framework can be further divided into a view layer, a viewmodel layer, and a data fetch layer. The data fetch layer can obtain relevant data for personalized recommendations through common gateway interfaces (CGI), local databases, and channels, and then provides this data to the viewmodel layer for adaptation processing, resulting in the adapted data. The adaptation process can be found in [reference needed]. Figure 5b This will not be elaborated further. After obtaining the adapted data provided by the adaptation layer, the view layer binds the adapted data to the components associated with the user page, resulting in components bound with the adapted data. Specifically, this binding refers to attaching the adapted data to the corresponding properties or states of the component. Next, the view layer renders the components bound with the adapted data to obtain the rendered result of the personalized recommendation content.

[0113] S406. Based on the rendering result of the personalized recommendation content, display the personalized recommendation content on the user page.

[0114] For a detailed implementation of step S406, please refer to Figure 2The implementation method of step S204 will not be described in detail here.

[0115] The data processing method provided in this application can be applied to various scenarios involving real-time personalized content recommendations; for example, information recommendations for different users in browsers, and industry information recommendations in social applications. The following explanation uses recommending industry information in a social application as an example to illustrate the data processing method provided in this application. In social applications, entry points to industry information pages can be displayed through message bars or other means. When the computer device detects that the probability of an entry point to an industry information page being triggered is greater than a probability threshold, it loads a shell of the rendering framework. When a user enters the industry information page, the computer device displays one or more entry points to industry information on the industry information page based on the recommendation message stream (e.g., each entry point to an industry information page corresponds to an information bar on the industry information page). The industry information recommended on the industry information page constitutes the personalized recommendation content. When the computer device detects that the probability of a target industry information being triggered is greater than a probability threshold, it preloads the data of the target industry information through the server (i.e., completes calculations related to the target industry information on the server). Further, when the target industry information is triggered, the computer device calls the rendering framework to obtain the data of the target industry information from the server, and performs adaptation processing on the data of the target industry information through the rendering framework to obtain the adapted data. Next, the rendering framework renders the adapted data to obtain the rendered industry information. The computer device then displays the industry information on the industry information page according to this rendering result. Furthermore, on the industry information page, the computer device can control the frequency of data fetching and preloading based on its performance and load. For details, please refer to [link / reference]. Figure 5a The data processing flow shown will not be elaborated further here.

[0116] As demonstrated by the data processing methods described above, by layering the rendering framework and the server-side, and by having the server-side, developed in a native language, perform the corresponding calculations, rendering efficiency can be guaranteed. This allows personalized recommendations to be pushed to users in a very short time, ensuring that users do not perceive any data fetching or UI rendering refresh when opening personalized recommendations. Furthermore, using a cross-platform development framework on the front end guarantees the consistency of personalized recommendations across different systems or platforms.

[0117] In this embodiment, in response to personalized recommendation content meeting the triggering condition, the rendering framework is invoked to obtain personalized recommendation content data from a server developed using a native language. The rendering framework is a cross-platform development framework. The data of the personalized recommendation content is adapted using the rendering framework to obtain adapted data. Then, the adapted data is rendered using the rendering framework to obtain the rendering result of the personalized recommendation content. Based on the rendering result, the personalized recommendation content is displayed on the user's page. It is evident that by layering the rendering framework and the server, using a cross-platform development framework as the rendering framework, and developing the server using a native language, consistent cross-platform rendering can be achieved while ensuring rendering efficiency. Furthermore, by preloading the data of the target personalized recommendation content, the response speed can be further improved, achieving seamless, instant page loading and further enhancing the user experience. After triggering the preloading of the target personalized recommendation content, the update status of the recommendation message stream is used to further determine whether to preload the data of the target personalized recommendation content, saving unnecessary computational resource overhead compared to blindly preloading.

[0118] The methods of the embodiments of this application have been described in detail above. In order to facilitate better implementation of the above solutions of the embodiments of this application, the apparatus of the embodiments of this application is provided below.

[0119] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a data processing device provided in an embodiment of this application. Figure 6 The data processing device shown can be mounted in a computer device, which can specifically be a terminal device. Figure 6 The data processing apparatus shown can be used to perform the above. Figure 2 and Figure 4 Some or all of the functionality described in the method embodiments. Please refer to [link / reference]. Figure 6 The data processing device includes:

[0120] The acquisition unit 601 is used to call the rendering framework to obtain the data of the personalized recommendation content from the server in response to the personalized recommendation content meeting the triggering conditions. The server is developed using a native language, and the rendering framework is a cross-platform development framework.

[0121] The processing unit 602 is used to adapt the data of personalized recommendation content through the rendering framework to obtain adapted data, which meets the data adaptation requirements of the components associated with the user page.

[0122] And it is used to render the adapted data through a rendering framework to obtain the rendering results of personalized recommendation content;

[0123] Display unit 603 is used to display personalized recommended content on the user page according to the rendering result of personalized recommended content.

[0124] In one embodiment, the processing unit 602 is further configured to:

[0125] When the data of the personalized recommendation content is detected to meet the preloading conditions, the personalized recommendation content data is preloaded by the server to obtain the personalized recommendation content data;

[0126] The preloading conditions include: the probability of personalized recommendation content being triggered is greater than a probability threshold.

[0127] In one embodiment, the processing unit 602 is configured to, when it is detected that the data of the personalized recommendation content meets the preloading conditions, preload the data of the personalized recommendation content through the server to obtain the data of the personalized recommendation content, specifically configured to:

[0128] Retrieve the recommendation message stream, which includes M identifiers of personalized recommendation content. The identifier of any personalized recommendation content is used to retrieve the data of that personalized recommendation content from the server. M is a positive integer.

[0129] If the identifier of at least one target personalized recommendation content is not included in the exposure progress identifier, the server will preload the data of at least one target personalized recommendation content to obtain the data of at least one target personalized recommendation content; the exposure progress identifier records the identifiers of the personalized recommendation content that have been exposed.

[0130] In one embodiment, the processing unit 602 is configured to preload data of at least one target personalized recommendation content from the server to obtain data of at least one target personalized recommendation content, specifically configured to:

[0131] Trigger the preloading of at least one target personalized recommendation content on the server side;

[0132] Based on the update status of the recommendation message stream, determine whether at least one target personalized recommendation content meets the preloading execution conditions;

[0133] When at least one target personalized recommendation content meets the preloading execution conditions, the server preloads the data of at least one target personalized recommendation content to obtain the data of at least one target personalized recommendation content.

[0134] In one implementation, the processing unit 602 is configured to determine, based on the update status of the recommendation message stream, whether at least one target personalized recommendation content meets the preloading execution conditions, specifically configured to:

[0135] If the recommendation message stream is updated, then at least one target personalized recommendation content is determined to meet the preloading execution conditions;

[0136] If the recommendation message stream is not updated and its duration is greater than the duration threshold, then at least one target personalized recommendation content is determined to meet the preloading execution conditions.

[0137] If the recommended message stream is not updated and its duration is less than or equal to the duration threshold, then at least one target personalized recommendation content does not meet the preloading execution conditions.

[0138] In one implementation, the processing unit 602 is used to adapt the personalized recommendation content data through a rendering framework to obtain adapted data, specifically for:

[0139] Construct at least one data unit based on the data from personalized recommendation content;

[0140] Based on the data adaptation requirements of the components associated with the user page, at least one data unit is transformed to obtain the adapted data.

[0141] In one implementation, the processing unit 602 is used to render the adapted data through a rendering framework to obtain a rendering result of personalized recommendation content, specifically for:

[0142] The adapted data is bound to the components associated with the user page to obtain the components bound with the adapted data.

[0143] Render the components that are bound to the adapted data to obtain the rendering result of personalized recommendation content.

[0144] In one implementation, the processing unit 602 is configured to, in response to the personalized recommendation content meeting the triggering condition, invoke the rendering framework to obtain personalized recommendation content data from the server, specifically for:

[0145] When the personalized recommendation content meets the triggering conditions, the data service on the server is called through the language interaction interface so that the server can obtain and return the personalized recommendation content data through lightweight computing.

[0146] Obtain personalized recommendation data returned by the server from the language interaction interface.

[0147] In one embodiment, the processing unit 602 is further configured to:

[0148] In response to the rendering framework meeting the warm-up conditions, the rendering framework is loaded with an empty shell.

[0149] Among them, empty shell loading is used to load the rendering framework code into system memory.

[0150] In one implementation, the rendering framework runs on the client, and the client and server adopt a layered architecture; the rendering framework includes a data fetch layer, an adaptation layer, and a view layer.

[0151] The data retrieval layer is used to retrieve personalized recommendation content data from the server; the adaptation layer is used to adapt the personalized recommendation content data; and the view layer is used to render the adapted data.

[0152] According to one embodiment of this application, Figure 2 and Figure 4 The data processing method shown may involve some steps that can be derived from... Figure 6 The data processing apparatus shown is executed by each unit within it. For example, Figure 2 Step S201 shown can be performed by Figure 6 The acquisition unit 601 shown is executed, and steps S202 and S203 can be performed by... Figure 6 The processing unit 602 shown executes the step S204, which can be performed by... Figure 6 The display unit 603 shown is executed. Figure 4 Step S403 shown can be performed by Figure 6 The acquisition unit 601 shown is executed, and steps S401, S402, S404, and S405 can be performed by... Figure 6 The processing unit 602 shown executes the step S406, which can be performed by... Figure 6 The display unit 603 shown is executed. Figure 6 The data processing apparatus shown can be constructed by combining each unit individually or entirely into one or more other units, or one or more of the units can be further divided into multiple functionally smaller units. This achieves the same operation without affecting the technical effects of the embodiments of this application. The above-mentioned units are based on logical function division. In practical applications, the function of one unit can be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of this application, the data processing apparatus may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0153] According to another embodiment of this application, a general-purpose computing device, such as a computer device including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM), can perform operations such as... Figure 2 and Figure 4 The computer program (including program code) for each step involved in the corresponding method shown, to construct such... Figure 6The data processing apparatus shown herein, and the data processing method for implementing the embodiments of this application, are described. A computer program may be recorded on, for example, a computer-readable recording medium, loaded onto the aforementioned computing device via the computer-readable recording medium, and executed therein.

[0154] Based on the same inventive concept, the principle and beneficial effects of the data processing device provided in the embodiments of this application in solving the problem are similar to the principle and beneficial effects of the data processing method in the embodiments of this application in solving the problem. For the sake of brevity, the principle and beneficial effects of the method implementation can be referred to.

[0155] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. The computer device may be a terminal device or a server. Figure 7 As shown, the computer device includes at least a processor 701, a communication interface 702, and a memory 703. The processor 701, communication interface 702, and memory 703 can be connected via a bus or other means. The processor 701 (or Central Processing Unit, CPU) is the computing and control core of the computer device. It can parse various instructions within the computer device and process various data. For example, the CPU can parse power-on / off commands issued by objects to the computer device and control the computer device to perform power-on / off operations; it can also transmit various interactive data between internal structures of the computer device, and so on. The communication interface 702 may optionally include standard wired interfaces or wireless interfaces (such as Wi-Fi, mobile communication interfaces, etc.), and can be used to send and receive data under the control of the processor 701; the communication interface 702 can also be used for data transmission and interaction within the computer device. The memory 703 is the storage device in the computer device, used to store programs and data. It can be understood that the memory 703 here can include the computer device's built-in memory, or it can include extended memory supported by the computer device. The memory 703 provides storage space for storing the operating system of the computer device, which may include, but is not limited to, Android, iOS, Windows Phone, etc. This application does not limit this.

[0156] This application embodiment also provides a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the processing system of the computer device. Furthermore, the storage space also stores computer programs suitable for loading and execution by the processor 701. It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0157] In one embodiment, processor 701 performs the following operations by running a computer program stored in memory 703:

[0158] When the personalized recommendation content meets the triggering conditions, the rendering framework is called to retrieve the personalized recommendation content data from the server. The server is developed using a native language, while the rendering framework is a cross-platform development framework.

[0159] The rendering framework is used to adapt the data of personalized recommendation content to obtain adapted data that meets the data adaptation requirements of the components associated with the user page.

[0160] The rendering framework is used to render the adapted data to obtain the rendering result of personalized recommendation content.

[0161] Based on the rendering results of the personalized recommendations, the personalized recommendations are displayed on the user's page.

[0162] As an optional embodiment, the processor 701, by running a computer program in the memory 703, also performs the following operations:

[0163] When the data of the personalized recommendation content is detected to meet the preloading conditions, the personalized recommendation content data is preloaded by the server to obtain the personalized recommendation content data;

[0164] The preloading conditions include: the probability of personalized recommendation content being triggered is greater than a probability threshold.

[0165] As an optional embodiment, when the personalized recommendation content data is detected to meet the preloading conditions, the processor 701 preloads the personalized recommendation content data through the server. A specific embodiment of obtaining the personalized recommendation content data is as follows:

[0166] Retrieve the recommendation message stream, which includes M identifiers of personalized recommendation content. The identifier of any personalized recommendation content is used to retrieve the data of that personalized recommendation content from the server. M is a positive integer.

[0167] If the identifier of at least one target personalized recommendation content is not included in the exposure progress identifier, the server will preload the data of at least one target personalized recommendation content to obtain the data of at least one target personalized recommendation content; the exposure progress identifier records the identifiers of the personalized recommendation content that have been exposed.

[0168] As an optional embodiment, the processor 701 preloads the data of at least one target personalized recommendation content through the server to obtain the data of at least one target personalized recommendation content. A specific embodiment of this is as follows:

[0169] Trigger the preloading of at least one target personalized recommendation content on the server side;

[0170] Based on the update status of the recommendation message stream, determine whether at least one target personalized recommendation content meets the preloading execution conditions;

[0171] When at least one target personalized recommendation content meets the preloading execution conditions, the server preloads the data of at least one target personalized recommendation content to obtain the data of at least one target personalized recommendation content.

[0172] As an optional embodiment, the processor 701 determines whether at least one target personalized recommendation content meets the preloading execution conditions based on the update status of the recommendation message stream. A specific embodiment of this is as follows:

[0173] If the recommendation message stream is updated, then at least one target personalized recommendation content is determined to meet the preloading execution conditions;

[0174] If the recommendation message stream is not updated and its duration is greater than the duration threshold, then at least one target personalized recommendation content is determined to meet the preloading execution conditions.

[0175] If the recommended message stream is not updated and its duration is less than or equal to the duration threshold, then at least one target personalized recommendation content does not meet the preloading execution conditions.

[0176] As an optional embodiment, the processor 701 adapts the personalized recommendation content data through a rendering framework to obtain the adapted data. A specific embodiment of this is as follows:

[0177] Construct at least one data unit based on the data from personalized recommendation content;

[0178] Based on the data adaptation requirements of the components associated with the user page, at least one data unit is transformed to obtain the adapted data.

[0179] As an optional embodiment, the processor 701 renders the adapted data through a rendering framework to obtain the rendering result of personalized recommendation content. A specific embodiment of this is as follows:

[0180] The adapted data is bound to the components associated with the user page to obtain the components bound with the adapted data.

[0181] Render the components that are bound to the adapted data to obtain the rendering result of personalized recommendation content.

[0182] As an optional embodiment, in response to the personalized recommendation content meeting the triggering condition, the processor 701 calls the rendering framework to obtain the personalized recommendation content data from the server.

[0183] When the personalized recommendation content meets the triggering conditions, the data service on the server is called through the language interaction interface so that the server can obtain and return the personalized recommendation content data through lightweight computing.

[0184] Obtain personalized recommendation data returned by the server from the language interaction interface.

[0185] As an optional embodiment, the processor 701, by running a computer program in the memory 703, also performs the following operations:

[0186] In response to the rendering framework meeting the warm-up conditions, the rendering framework is loaded with an empty shell.

[0187] Among them, empty shell loading is used to load the rendering framework code into system memory.

[0188] As an optional implementation, the rendering framework runs on the client side, and the client and server adopt a layered architecture; the rendering framework includes a data fetch layer, an adaptation layer, and a view layer;

[0189] The data retrieval layer is used to retrieve personalized recommendation content data from the server; the adaptation layer is used to adapt the personalized recommendation content data; and the view layer is used to render the adapted data.

[0190] Based on the same inventive concept, the principle and beneficial effects of the computer device provided in the embodiments of this application in solving the problem are similar to the principle and beneficial effects of the data processing method in the embodiments of this application in solving the problem. Please refer to the principle and beneficial effects of the method implementation. For the sake of brevity, they will not be repeated here.

[0191] This application also provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and to execute the data processing method described in the above method embodiments.

[0192] This application also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the data processing method described above.

[0193] The steps in the method of this application embodiment can be adjusted, combined, or deleted according to actual needs.

[0194] The modules in the device of this application embodiment can be merged, divided, and deleted according to actual needs.

[0195] In the embodiments of this application, the term "module" or "unit" refers to a computer program or part of a computer program with a predetermined function, which works together with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0196] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0197] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Those skilled in the art will understand that all or part of the processes for implementing the above embodiments and equivalent variations made in accordance with the claims of this application are still within the scope of this application.

Claims

1. A data processing method, characterized in that, The method includes: In response to the personalized recommendation content meeting the triggering condition, the rendering framework is invoked to obtain the data of the personalized recommendation content from the server. The server is developed using a native language, and the rendering framework is a cross-platform development framework. The rendering framework is used to adapt the data of the personalized recommendation content to obtain adapted data, which meets the data adaptation requirements of the components associated with the user page. The rendering framework is used to render the adapted data to obtain the rendering result of the personalized recommendation content. The personalized recommendation content is displayed on the user page according to the rendering result of the personalized recommendation content.

2. The method as described in claim 1, characterized in that, The method further includes: When the data of the personalized recommendation content is detected to meet the preloading conditions, the personalized recommendation content data is preloaded by the server to obtain the personalized recommendation content data; The preloading condition includes: the probability of the personalized recommendation content being triggered is greater than a probability threshold.

3. The method as described in claim 2, characterized in that, When the personalized recommendation content data is detected to meet the preloading conditions, the personalized recommendation content data is preloaded by the server to obtain the personalized recommendation content data, including: Obtain the recommendation message stream, which includes M identifiers of personalized recommendation content. The identifier of any personalized recommendation content is used to obtain the data of that personalized recommendation content from the server, where M is a positive integer. If the identifier of at least one target personalized recommendation content is not included in the exposure progress identifier, the server preloads the data of the at least one target personalized recommendation content to obtain the data of the at least one target personalized recommendation content; the exposure progress identifier records the identifiers of the personalized recommendation content that have been exposed.

4. The method as described in claim 3, characterized in that, The step of preloading the data of the at least one target personalized recommendation content through the server to obtain the data of the at least one target personalized recommendation content includes: Trigger the preloading of at least one target personalized recommendation content on the server side; Based on the update status of the recommendation message stream, determine whether the at least one target personalized recommendation content meets the preloading execution conditions; When the at least one target personalized recommendation content meets the preloading execution conditions, the data of the at least one target personalized recommendation content is preloaded by the server to obtain the data of the at least one target personalized recommendation content.

5. The method as described in claim 4, characterized in that, The step of determining whether the at least one target personalized recommendation content meets the preloading execution conditions based on the update status of the recommendation message stream includes: If the recommendation message stream is updated, then it is determined that the at least one target personalized recommendation content meets the preloading execution conditions; If the recommended message stream is not updated and its duration is greater than the duration threshold, then the at least one target personalized recommendation content is determined to meet the preloading execution condition. If the recommended message stream is not updated and its duration is less than or equal to the duration threshold, then it is determined that the at least one target personalized recommendation content does not meet the preloading execution conditions.

6. The method as described in claim 1, characterized in that, The process of adapting the personalized recommendation content data through the rendering framework to obtain adapted data includes: Based on the data of the personalized recommendation content, construct at least one data unit; Based on the data adaptation requirements of the components associated with the user page, the at least one data unit is transformed to obtain the adapted data.

7. The method as described in claim 1, characterized in that, The step of rendering the adapted data through the rendering framework to obtain the rendering result of the personalized recommendation content includes: The adapted data is bound to the component associated with the user page to obtain the component bound with the adapted data. The component bound to the adapted data is rendered to obtain the rendering result of the personalized recommendation content.

8. The method as described in claim 1, characterized in that, The step of responding to personalized recommendations meeting the triggering conditions by calling the rendering framework to retrieve the personalized recommendation content data from the server includes: In response to the personalized recommendation content meeting the triggering conditions, the data service in the server is called through the language interaction interface, so that the server obtains and returns the data of the personalized recommendation content through lightweight computing. The personalized recommendation content data returned by the server is obtained from the language interaction interface.

9. The method as described in claim 1, characterized in that, The method further includes: In response to the rendering framework meeting the preheating conditions, the rendering framework is loaded with an empty shell. The empty shell loading is used to load the code of the rendering framework into system memory.

10. The method according to any one of claims 1-9, characterized in that, The rendering framework runs on the client side, and the client and the server side adopt a layered architecture; The rendering framework includes a data fetch layer, an adaptation layer, and a view layer; The data retrieval layer is used to obtain data of the personalized recommendation content from the server. The adaptation layer is used to adapt the data of the personalized recommendation content; the view layer is used to render the adapted data.

11. A data processing apparatus, characterized in that, The data processing device includes: The acquisition unit is used to call the rendering framework to obtain the data of the personalized recommendation content from the server in response to the personalized recommendation content meeting the triggering condition. The server is developed using a native language, and the rendering framework is a cross-platform development framework. The processing unit is used to adapt the data of the personalized recommendation content through the rendering framework to obtain adapted data, wherein the adapted data meets the data adaptation requirements of the components associated with the user page. And the rendering result of the personalized recommendation content is obtained by rendering the adapted data through the rendering framework. The display unit is used to display the personalized recommendation content on the user page according to the rendering result of the personalized recommendation content.

12. A computer device, characterized in that, include: A memory, wherein a computer program is stored; A processor for loading the computer program to implement the data processing method as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1-10.

14. A computer program product, characterized in that, The computer program product includes a computer program adapted to be loaded by a processor and execute the data processing method as described in any one of claims 1-10.

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