An information processing method, apparatus, electronic device, and storage medium

Abstract

This application discloses an information processing method, apparatus, electronic device, and storage medium. By acquiring recommendation association data of a target media resource during the resource recommendation stage and user interaction data generated during the consumption stage of the target media resource after the resource recommendation stage, resource detail information corresponding to the target media resource is generated based on the recommendation association data and user interaction data. This resource detail information provides an intuitive display of the traffic data of the target media resource from the resource recommendation stage to the consumption stage, making the traffic data of the media resource from the resource recommendation stage to the consumption stage more transparent. Developers can intuitively observe the traffic conversion of the media resource, and accurately investigate and optimize the recommendation stage of the media resource based on the resource detail information, thereby effectively improving the recommendation accuracy of the media resource.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more specifically to an information processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] To meet people's pursuit of spiritual enrichment, media resource platforms that can be operated on terminals and are available for users to watch or listen to have emerged. Examples include video applications (Apps) that allow users to watch videos, and music applications that allow users to listen to songs, music, audiobooks, and other music.

[0003] Currently, existing media resource applications use collected user behavior data to call resource recommendation models, resulting in a candidate media resource set composed of one or more media resource outcomes. After obtaining the candidate media resource set, filtering, sorting, and weighting operations can be performed according to rules for different business scenarios, thereby recommending corresponding media resources to users based on different business scenarios and enriching the user experience. For example, music applications recommend different music resources based on business scenarios (such as daily recommendations, style recommendations, personal FM, etc.). However, existing technologies suffer from the problem of opaque traffic data throughout the entire link from the resource recommendation stage to the consumption stage. This prevents developers of media resource applications from intuitively observing the traffic conversion of media resources, and makes it difficult for them to accurately investigate and optimize the recommendation stage, resulting in low recommendation accuracy. Summary of the Invention

[0004] This application provides an information processing method, apparatus, electronic device, and storage medium that makes the traffic data of media resources more transparent throughout the entire chain from the resource recommendation stage to the consumption stage. Developers can intuitively observe the traffic conversion of media resources, and accurately investigate and optimize the recommendation stage of media resources based on resource details, thereby effectively improving the accuracy of media resource recommendations.

[0005] In a first aspect, embodiments of this application provide an information processing method, including: Based on the resource identifier of the target media resource, the recommendation association data of the target media resource in the resource recommendation stage is obtained. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. Based on the resource identifier, obtain user interaction data generated by the target media resource in the consumption stage after the resource recommendation stage, wherein the user interaction data includes user interaction behavior with the target media resource during the consumption stage; Based on the recommended association data and the user interaction data, resource details information corresponding to the target media resource is generated. The resource details information is used to indicate the data corresponding to the complete link of the target media resource from the resource recommendation stage to the consumption stage, so as to display the resource details information through the graphical user interface of the terminal device when the resource details information is triggered to be displayed.

[0006] Secondly, embodiments of this application provide an information processing apparatus, including: The first acquisition unit is used to acquire recommendation association data of the target media resource in the resource recommendation stage based on the resource identifier of the target media resource. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. The second acquisition unit is used to acquire user interaction data generated by the target media resource in the consumption stage after the recommendation stage, based on the resource identifier, wherein the user interaction data includes user interaction behavior with the target media resource during the consumption stage. The generation unit is used to generate resource detail information corresponding to the target media resource based on the recommended association data and the user interaction data. The resource detail information is used to display the data corresponding to the complete link of the target media resource from the resource stage to the consumption stage, so as to display the resource detail information through the graphical user interface of the terminal device when the resource detail information is triggered to be displayed.

[0007] Thirdly, embodiments of this application also provide an electronic device, including a memory storing multiple instructions; a processor loads instructions from the memory to execute the steps of any of the information processing methods provided in embodiments of this application.

[0008] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a plurality of instructions adapted for loading by a processor to execute the steps of any of the information processing methods provided in embodiments of this application.

[0009] Fifthly, embodiments of this application also provide a computer program product, including a computer program or instructions, which, when executed by a processor, implement the steps in any of the information processing methods provided in embodiments of this application.

[0010] The solution adopted in this application embodiment can obtain recommendation association data of the target media resource in the resource recommendation stage and user interaction data generated in the consumption stage after the resource recommendation stage. Based on the recommendation association data and user interaction data, resource detail information corresponding to the target media resource is generated. The resource detail information can be used to intuitively display the traffic data of the target media resource from the resource recommendation stage to the consumption stage, making the traffic data of the media resource from the resource recommendation stage to the consumption stage more transparent. Developers can intuitively observe the traffic conversion of the media resource. Developers can accurately check and optimize the recommendation stage of the media resource based on the resource detail information, thereby effectively improving the recommendation accuracy of the media resource. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a scene diagram of the virtual character processing system provided in the embodiments of this application. Figure 2 This is a schematic flowchart of one embodiment of the information processing method provided in this application. Figure 3 This is a schematic diagram of resource details information for the card image style provided in the embodiments of this application; Figure 4 This is a schematic diagram of resource details information for the funnel chart pattern provided in the embodiments of this application; Figure 5 This is a schematic diagram of resource details information in the table format provided in the embodiments of this application; Figure 6 This is a schematic diagram of resource details information for the trend chart style provided in the embodiments of this application; Figure 7 This is a schematic flowchart of another embodiment of the information processing method provided in this application. Figure 8 This is a schematic flowchart of another embodiment of the information processing method provided in this application. Figure 9This is a schematic flowchart of another embodiment of the information processing method provided in this application. Figure 10 This is a schematic diagram of the structure of the information processing device provided in the embodiments of this application; Figure 11 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0013] 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 a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. At the same time, in the description of the embodiments of this application, the terms "first," "second," etc., are only used to distinguish descriptions and should not be construed as indicating or implying relative importance. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0014] This application provides an information processing method, apparatus, electronic device, and computer-readable storage medium. Specifically, this embodiment will be described from the perspective of an information processing apparatus, which can be integrated into an electronic device. That is, the information processing method of this application embodiment can be executed by an electronic device. Optionally, the electronic device may include a terminal device. The terminal device may be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, game console, or personal computer (PC), etc.

[0015] The information processing method provided in this application can be applied to systems such as conference interaction systems. This conference interaction system may include terminal devices and servers. A terminal can be a device that includes both receiving and transmitting hardware, i.e., a device with receiving and transmitting hardware capable of performing bidirectional communication over a bidirectional communication link. The terminal device and the server can communicate bidirectionally via a network.

[0016] Optionally, the server can be a standalone server, or a server network or server cluster, including but not limited to computers, network hosts, single network servers, multiple network server sets, or cloud servers composed of multiple servers. Cloud servers consist of a large number of computers or network servers based on cloud computing.

[0017] In one embodiment of this disclosure, the information processing method can run on a local terminal device or a server. When the conference interaction method runs on a server, the method can be implemented and executed based on a cloud interaction system, wherein the cloud interaction system includes a server and client devices.

[0018] Please see Figure 1 , Figure 1 This is a schematic diagram of an information processing system provided in an embodiment of this application. The system may include at least one terminal, at least one server, at least one database, and a network. A user's terminal can connect to different servers via the network. The terminal is any device with computing hardware capable of supporting and executing software products corresponding to model generation. Furthermore, when the system includes multiple terminals, multiple servers, and multiple networks, different terminals can connect to each other through different networks and servers. The network can be a wireless network or a wired network, such as a wireless local area network (WLAN), local area network (LAN), cellular network, 2G network, 3G network, 4G network, 5G network, etc. Additionally, different terminals can also connect to other terminals or servers using their own Bluetooth networks or hotspot networks. For example, multiple users can connect online through different terminals via appropriate networks and synchronize with each other to support multi-user access. Furthermore, the system may include multiple databases coupled to different servers, and information related to the operating environment can be continuously stored in the databases while different users are using the system online.

[0019] In an alternative implementation, the local terminal device stores a music application and uses it to display a screen (i.e., a page of the music application). The local terminal device is used to interact with a user using the music application via a graphical user interface (GUI), i.e., conventionally downloading, installing, and running the music application via an electronic device. The local terminal device can provide the GUI to the user in various ways, such as rendering it on a terminal display screen or providing it to the user via holographic projection. For example, the local terminal device may include a display screen for displaying the GUI, which includes a screen, and a processor for running the music application, generating the GUI, and controlling the display of the GUI on the screen.

[0020] The following detailed description is provided in conjunction with the accompanying drawings. In this embodiment, the execution subject is a terminal device as an example. It should be noted that the order of description in the following embodiments is not intended to limit the preferred order of the embodiments. Although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be performed in a different order than that shown in the accompanying drawings.

[0021] Please see Figure 2 , Figure 2 This is a flowchart illustrating an information processing method provided in an embodiment of this application. The specific flow of this information processing method can be summarized in steps 101 to 103, wherein: Step 101: Based on the resource identifier of the target media resource, obtain the recommendation association data of the target media resource in the resource recommendation stage. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage.

[0022] The target media resources include target recommended audio (e.g., songs, podcasts, etc.) and / or target recommended audio sets (e.g., playlists), wherein the target audio recommendation set includes at least two recommended audio files.

[0023] Optionally, the target media resource can also be a video.

[0024] In one embodiment, the resource recommendation stage may include a recall sub-stage, a coarse ranking sub-stage, a fine ranking sub-stage, and a re-ranking sub-stage. Based on the resource identifier of the target media resource, recommendation association data of the target media resource in the resource recommendation stage is obtained. Specifically, traffic data corresponding to the target media resource in the recall sub-stage, coarse ranking sub-stage, fine ranking sub-stage, and re-ranking sub-stage may be obtained. Optionally, an exposure sub-stage may also be included.

[0025] Step 102: Based on the resource identifier, obtain the user interaction data generated by the target media resource in the consumption stage after the resource recommendation stage, wherein the user interaction data includes the user's interaction behavior with the target media resource in the consumption stage.

[0026] In the embodiments of this application, the interactive behavior may include click behavior, play behavior, like behavior, favorite behavior, download behavior, etc., which are only examples for illustration, and more examples will not be elaborated.

[0027] Specifically, based on the resource identifier, user interaction data generated by the target media resource during the consumption stage after the resource recommendation stage can be obtained. For example, the number of times clicks, playback, likes, favorites, and downloads are generated can be obtained.

[0028] Step 103: Based on the recommended association data and the user interaction data, generate resource details information corresponding to the target media resource. The resource details information is used to indicate the data corresponding to the complete link of the target media resource from the resource recommendation stage to the consumption stage, so as to display the resource details information through the graphical user interface of the terminal device when the resource details information is triggered to be displayed.

[0029] Specifically, the step "generating resource details information corresponding to the target media resource based on the recommended association data and the user interaction data" includes: Based on the target display style, the recommended association data, and the user interaction data, resource details information of the target display style corresponding to the target media resource is generated.

[0030] The target display style includes at least one of the following: card chart style, funnel chart style, table chart style, and trend chart style.

[0031] For example, please see Figure 3 , Figure 3 This section presents a diagrammatic representation of resource details in a card-like format. This card-style resource details information provides a macro-level overview, reflecting the real-time data status of the current resource ID (song, playlist) and helping business users quickly understand overall traffic performance and health. Key metrics such as total recall, total recommendations, total impressions, total plays, and total likes are displayed in a prominent card format, along with year-over-year and month-over-month (compared to the same period yesterday and last week) changes. Key conversion rates: This displays core funnel conversion rates, such as recommendation rate (number of recommendations / number of recalls), impression rate (number of impressions / number of recommendations), play rate (number of plays / number of impressions), and like rate (number of likes / number of plays).

[0032] For example, please see Figure 4 , Figure 4This is a diagram illustrating resource details in a funnel chart style. The funnel chart visually displays the conversion process of a song from recall -> filtering -> sorting -> recommendation -> exposure -> click -> play -> like. Through this funnel chart, business users can clearly see the conversion rate and churn rate at each stage, quickly identifying bottlenecks in traffic loss. The metrics in the funnel chart will use Chinese characters, such as "Recall Count" and "Recommendation Count," and the displayed funnel stages can be adjusted according to needs. For example, filter_cnt, collect_cnt, and download_cnt can be selectively removed to focus on the core conversion path. Users can also use filtering controls to select different songs, scenarios, channels, ALGs, and time ranges to view their corresponding traffic funnel situations, enabling in-depth analysis of specific traffic segments.

[0033] For example, please see Figure 5 , Figure 5 This is a diagram illustrating resource details in a tabular format. The tabular format provides multi-dimensional data drill-down analysis capabilities, helping business stakeholders conduct more detailed attribution analysis. Through multi-dimensional slicing, users can perform detailed slicing analysis of business metrics data based on dimensions such as business scenarios (e.g., daily push, FM, MGC, Heartbeat, style, etc.), channels (e.g., organic traffic, song push traffic, paid traffic, etc.), and algorithms (ALG). Detailed metric display: The sliced ​​table will display the recall, ranking, recommendation, exposure, clicks, plays, effective plays, completions, and hearts for each dimension's enumerated values, as well as the corresponding ratio metrics (recall rate, ranking rate, push rate, play rate, effective play rate, completion rate, heart rate). Sorting and highlighting operations are also supported; the table will be sorted in descending order of play count for quick identification of high-value content. Additionally, abnormal metrics can be highlighted to alert business stakeholders.

[0034] For example, please see Figure 6 , Figure 6This is a schematic diagram of resource details information presented in a trend chart style. The trend chart style resource details information displays the trend changes of hourly and minute-level indicators, helping to determine the causes of abnormal traffic fluctuations and evaluate the effectiveness of operational activities or control strategies. It also allows for multi-indicator trend comparison. Specifically, multiple indicators (such as impressions, clicks, plays, and likes) can be displayed on the same line chart (i.e., the trend chart style resource details information), facilitating comparative analysis. Flexible filtering and aggregation are also supported. The trend chart supports filtering controls, allowing users to switch between scenarios, channels, ALGs, and time ranges for in-depth analysis of traffic change trends under specific dimensions. Simultaneously, it supports aggregation and display at different time granularities (minutes, hours, days). Through these visual reports, this invention provides business users with comprehensive, real-time, and multi-dimensional traffic funnel analysis capabilities, transforming complex data problems into intuitive business insights, greatly improving the efficiency and accuracy of problem diagnosis.

[0035] Based on the above description, the information processing method of this application will be further illustrated by the following examples. Specific embodiments of the information processing method are described below.

[0036] In one embodiment, before the step "obtaining the recommendation association data of the target media resource in the resource recommendation stage based on the resource identifier of the target media resource", the method further includes: In response to a resource recommendation event for a target business scenario, the target media resource is determined from multiple candidate media resources through a resource recommendation system based on the resource recommendation logic of the target business scenario.

[0037] Specifically, the resource recommendation system includes multiple recommendation subsystems, each corresponding to a sub-stage, and the method further includes: The recommendation subsystems are subjected to data tracking operations to collect media resources in each target business scenario and data in each sub-stage of the resource recommendation stage.

[0038] The sub-stages include the recall sub-stage, the coarse arrangement sub-stage, the fine arrangement sub-stage, and the re-arrangement sub-stage.

[0039] Furthermore, the method also includes: The collected media resources in each target business scenario, the data in each of the sub-stages of the resource recommendation stage, and the user interaction data generated in the consumption stage after the resource recommendation stage are associated to establish a relationship between the data in each of the sub-stages and the user interaction data.

[0040] The consumption stage can include sub-stages such as exposure, clicks, plays, likes, favorites, and downloads, with corresponding user interaction data collected for each sub-stage.

[0041] In one specific instance, the method further includes: Aggregate and / or accumulate the media resources in each of the target business scenarios and the data in each of the sub-stages of the resource recommendation stage to obtain the traffic data of each media resource in each of the sub-stages. The user interaction data generated in the consumption stage after the resource recommendation stage for each of the target business scenarios is aggregated and / or accumulated to obtain the processed user interaction data of each of the media resources in the consumption stage.

[0042] Optionally, the method further includes: Traffic data of each media resource in each sub-stage, and user interaction data after processing of each media resource in the consumption stage, are stored in a preset database.

[0043] In one specific embodiment, obtaining the recommendation association data of the target media resource in the resource recommendation stage based on the resource identifier of the target media resource includes: In response to the resource information display event for the target media resource, based on the resource identifier of the target media resource, the traffic data of the target media resource in each sub-stage of the resource recommendation stage is obtained from the preset database and used as the recommendation association data of the target media resource in the resource recommendation stage.

[0044] Optionally, in response to the resource information display event for the target media resource, retrieving traffic data of the target media resource in each sub-stage of the resource recommendation stage from the preset database based on the resource identifier of the target media resource, and using this data as the recommendation association data of the target media resource in the resource recommendation stage, includes: In response to a resource information display event for the target media resource, based on the specified time information indicated by the resource information display event and the resource identifier of the target media resource, traffic data of the target media resource in each sub-stage of the resource recommendation stage is obtained from the preset database and used as recommendation association data of the target media resource in the resource recommendation stage.

[0045] In summary, this application provides an information processing method that can acquire recommendation-related data of a target media resource during the resource recommendation stage and user interaction data generated during the consumption stage after the resource recommendation stage. Based on the recommendation-related data and user interaction data, resource detail information corresponding to the target media resource is generated. This resource detail information provides a clear view of the traffic data of the target media resource throughout the entire chain from the resource recommendation stage to the consumption stage, making the traffic data of the media resource more transparent. Developers can intuitively observe the traffic conversion of the media resource and accurately investigate and optimize the recommendation stage of the media resource based on the resource detail information, thereby effectively improving the accuracy of media resource recommendations.

[0046] Based on the above description, the information processing method of this application will be further illustrated with examples below. For example, please refer to... Figure 7 The system architecture diagram shows five main parts. The first part consists of existing scenarios within the Cloud Music App, such as daily recommendations and personal FM. When a user visits these scenarios, song recommendations are triggered. The second part is the recommendation engine based on the Mpp framework. Simultaneously, user behavior data (such as playback and clicks) in these scenarios is collected and sent to a Kafka message queue. The second part is the recommendation engine, which presents songs of interest to the user through four stages: recall (including various recall models and strategies) -> coarse ranking -> fine ranking -> re-ranking. Data tracking is performed in all four stages, and after serialization and compression, the algorithmic tracking data is sent to the Kafka message queue. The third part is the Kafka consumer queue, a typical producer-consumer model, supporting producers sending messages and consumers receiving messages. The fourth part is the Flink data task, which can consume, clean, and transform data in real time. It extracts data from the Kafka message queue, processes it, and finally stores it in the Doris database. The fifth part is Doris and the reporting system. Doris provides a powerful foundation for data reports with its strong data analysis capabilities. Data reports only need to be displayed in different styles of charts according to requirements.

[0047] Kafka is a distributed streaming data platform primarily used for real-time data pipelines and stream processing. Its core functionality is the efficient publishing, subscribing, storage, and processing of streaming data. It is characterized by high throughput, low latency, and strong scalability, and is commonly used for log collection, event sourcing, and message queues. Flink is a distributed stream processing framework that supports both real-time and batch processing tasks. Its core functionality is the efficient processing of unbounded streaming data and bounded batch data. It is characterized by low latency, high throughput, and exactly-once semantics, making it suitable for complex event processing, real-time analytics, and ETL tasks. Doris, or Apache Doris, is a high-performance, real-time analytical database based on an MPP architecture. It is known for its efficiency, simplicity, and unified approach, returning query results for massive datasets in sub-second response times. It supports not only high-concurrency point queries but also high-throughput complex analytical scenarios.

[0048] Based on the above description, the information processing method of this application will be further illustrated with examples below. For example, please refer to... Figure 8 The recommendation system is responsible for recommending songs to users. At the same time, it records the distribution of songs and user behavior at each stage of the recommendation system. The data collection module collects the data and hands it over to the data processing module. The data processing module links the two data streams (recommendation system algorithm data and user behavior data) together to form a flow funnel. Finally, the data analysis module displays the data in the form of charts, which can clearly express the information of each stage of song distribution.

[0049] Based on the above description, the information processing method of this application will be further illustrated with examples below. For example, please refer to... Figure 9 The specific implementation of this information processing method is as follows: (1) Build a recommendation system algorithm tracking framework and report tracking logs in accordance with the specifications.

[0050] To achieve end-to-end monitoring of the song traffic funnel, we first implemented refined data tracking at various key stages of the song recommendation system. This includes not only recording user behavior but, more importantly, highlighting the decision-making processes within the song recommendation system's algorithms.

[0051] Specifically, firstly, a standard algorithm tracking specification is defined, and then custom reporting tracking points are defined at multiple stages of the song recommendation system. The tracking specification mainly includes: A unified event tracking ID system: Define unified IDs and naming rules for core entities and attributes such as songs, users, algorithms, scenarios, and channels to avoid data ambiguity.

[0052] Phased event tracking: Define different event types and required reporting fields for key stages of the song recommendation system, such as recall, filtering, sorting, reordering, exposure, clicks, playback, likes, favorites, and downloads.

[0053] Data pass-through mechanism: Unique identifiers such as `trace_id` (request ID) are introduced to achieve end-to-end data pass-through from user request initiation, through internal processing within the recommendation system, to final user behavior. This enables subsequent data analysis to accurately attribute user behavior to specific algorithmic decisions and recommendation pathways.

[0054] Data format standardization: Efficient and scalable serialization protocols such as Protobuf are used to define the structure of the event tracking data, ensuring efficient data transmission and convenient parsing. Protobuf's schema definition enforces data format constraints, reducing data errors. Asynchronous reporting mechanism: Event tracking data is reported to Kafka asynchronously, avoiding performance impact on the core business logic of the recommendation system.

[0055] Specifically, the key data points and data content are implemented through refined data tracking across the entire recommendation system, primarily capturing the following types of key data: The recall phase's tracking points mark the starting point of the traffic funnel, reflecting the recall capability and coverage of the recommendation algorithm. It records the set of songs recalled by the recommendation algorithm system based on user interests, content features, and contextual information.

[0056] The coarse ranking stage records the collection of songs that have been removed after recall and various business rules, strategies, or security filters (such as copyright filtering, blacklist filtering, duplicate filtering, etc.). The songs are then sorted according to a preliminary ranking result, and the songs at the top of the ranking are selected. The data collected at this stage can be used to analyze the rationality of the filtering rules, the efficiency of the filtering, and the impact on traffic, and help identify unnecessary or excessive filtering after the coarse ranking model has processed the data.

[0057] The fine-ranking stage records a smaller set of songs after being processed by a better, more powerful, and more insightful fine-ranking model that better reflects user preferences.

[0058] The reordering phase tracking records the final ranking result after special weighting and business logic, recording the final list of recommendations sent to the user. This is usually the recommendation list that the user sees on the client interface, the content that the user can actually interact with.

[0059] During the exposure phase, event tracking is used to record the actual events in which users see the song on the client interface. Exposure is the first key link in the user behavior funnel, directly impacting subsequent clicks and playback.

[0060] During the playback phase, tracking points are used to record whether a song starts playing after a user clicks on it, whether it is a valid playback (listening time >= 15 seconds), and whether the entire song is played.

[0061] Tracking data for user engagement with songs via actions like "like," "favorite," and "download" is crucial. This data reflects a user's high level of appreciation for the song and is a key indicator of user loyalty and content value.

[0062] The main data points for the above eight stages include song ID (song_id), algorithm identifier (alg), scene (scene), traffic distribution channel (channel), user ID (user_id), unique request ID (trace_id), timestamp (timestamp), stage type (stage), and user specific behavior event (action).

[0063] (2) Collect real-time data.

[0064] This application embodiment achieves real-time data collection by consuming data from the distributed message queue Apache Kafka. Kafka, as a high-throughput, low-latency distributed messaging system, can handle the massive amounts of event tracking logs and user behavior logs generated by the recommendation system. Specifically, the collected data streams include real-time user behavior data streams and event tracking log data streams from the algorithm recommendation system. The real-time user behavior data stream includes user behavior logs (UA logs) reported by the client, such as events like exposure (_ev), clicks (_ec), plays, likes, favorites, and downloads. These logs are typically stored in a specific Kafka topic in JSON or other structured text formats. The algorithm recommendation system event tracking log data stream includes algorithm-side event tracking logs reported by the algorithm recommendation system, such as events like recall, filtering, and ranking. These logs are typically stored in another Kafka topic in Protobuf binary format and contain key link information such as trace_id. This application's embodiments can also perform data cleaning and standardization, parsing the original user behavior logs and extracting key fields from complex user behavior log strings, such as user_id (user ID), item_id (song ID), action (specific user behavior event), timestamp, scene, channel (traffic distribution channel), and alg (algorithm identifier). For Protobuf serialized logs, deserialization is required according to a predefined schema. The extracted fields are converted to the correct data types (e.g., string to number, timestamp formatting) and data validation is performed to filter out dirty data with incorrect formatting or missing key fields.

[0065] Next, data association and attribution operations are performed. The trace_id is used to associate the algorithm-side event tracking data (recall, filtering, ranking) with the user-side behavioral data (exposure, clicks, plays, likes, etc.). Although this data may come from different Kafka topics and have slight differences in arrival time, it is possible to associate all related events under the same user request based on trace_id, forming a complete traffic funnel chain. Scene attribution is based on the spm_no field in the user behavior log, using a configurable mapping table to map the original scene and traffic distribution channel to a unified, business-understandable scene and channel dimension. For example, spm_no like'%cell_song|mod_daily_recommend_song_list|page_daily_recommend%' is attributed to the daily recommendation scene in the algorithm recommendation system.

[0066] (3) Real-time data processing.

[0067] The real-time data processing module is key to achieving minute-level traffic funnel observation in this invention. This invention employs a real-time data stream processing architecture based on Apache Flink, constructing two main real-time data stream processing links to efficiently clean, transform, correlate, and aggregate massive amounts of raw data collected from Kafka message queues. The goal of this stage is to transform scattered, heterogeneous raw log data into structured traffic funnel metric data that can be directly used for analysis.

[0068] Specifically, sliding window aggregation can be performed. Flink uses time windows to aggregate data. For example, a 1-minute sliding window can be set to perform real-time counting and aggregation of events under each combination of the following dimensions: item_id (song ID), scene (scene), channel (traffic distribution channel), alg (algorithm identifier), dt (day-level date), hh (hour-level date), and mm (minute-level date). Furthermore, it is possible to perform metric accumulation processing, accumulating various flow funnel metrics within each window, including: Algorithm-side metrics: recall count (recall_cnt), filter count (filter_cnt), ranking count (rank_cnt), and distribution / recommendation count (topn_cnt); User behavior metrics: Impressions (impress_cnt), Clicks (click_cnt), Plays (play_cnt), Effective Plays (effective_play_cnt), Completed Plays (full_play_cnt), Hearts (red_cnt), Favorites (sub_cnt), Downloads (download_cnt). Finally, the data import operation is performed. The aggregated data, processed by Flink, is written to tables in the high-performance OLAP database Apache Doris in real time. Flink-to-Doris writes are typically implemented through the StreamLoad interface provided by Doris, which supports high concurrency and high throughput data import, meeting the stringent performance requirements of real-time analysis. This design ensures minimal end-to-end latency from data generation to queryability, providing a solid foundation for subsequent real-time data analysis and visualization. Doris table optimization is also possible, leveraging the advantages of Doris's aggregation model. It allows users to define aggregation rules during data import, automatically merging data with the same aggregation key. This significantly reduces storage space and speeds up queries because aggregation operations are completed during data writing. Materialized views are also supported by Doris, allowing users to pre-calculate and store query results. For frequently queried fixed-dimensional aggregations, materialized views can significantly improve query performance, achieving a "space-for-time" optimization strategy. This data storage and analysis solution combines Flink's real-time processing capabilities with Doris's high-performance OLAP features, ensuring efficient data writing, real-time aggregation, and fast querying. This provides a solid foundation for subsequent data analysis and visualization, and effectively solves the data latency and performance problems that may be caused by dual-stream join in traditional data solutions.

[0069] (4) Data Analysis. This application utilizes the powerful capabilities of the high-performance real-time analytical database Apache Doris, combined with reporting tools, to visualize the data in Doris, enabling real-time monitoring of business metrics and in-depth data mining and trend analysis. The data analysis reports are designed to provide business users with comprehensive, real-time, and multi-dimensional traffic funnel analysis capabilities, greatly improving the efficiency and accuracy of problem diagnosis. A series of core data analysis reports are designed to meet different levels of analysis needs, specifically displaying resource details in card chart style, funnel chart style, table chart style, and trend chart style.

[0070] In summary, this application provides an information processing method that can acquire recommendation association data of a target media resource during the resource recommendation stage and user interaction data generated during the consumption stage of the target media resource after the resource recommendation stage. Based on the recommendation association data and user interaction data, resource detail information corresponding to the target media resource is generated. This resource detail information allows for a direct display of the traffic data of the target media resource throughout the entire chain from the resource recommendation stage to the consumption stage, making the traffic data of the media resource more transparent. Developers can intuitively observe the traffic conversion of the media resource and accurately investigate and optimize the recommendation stage of the media resource based on the resource detail information, thereby effectively improving the recommendation accuracy of the media resource.

[0071] This embodiment also provides an information processing device, which can be specifically integrated into a terminal device. For example, such as Figure 10 As shown, the information processing device may include: The first acquisition unit 201 is used to acquire recommendation association data of the target media resource in the resource recommendation stage based on the resource identifier of the target media resource. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. The second acquisition unit 202 is used to acquire user interaction data generated by the target media resource in the consumption stage after the recommendation stage, based on the resource identifier, wherein the user interaction data includes user interaction behavior with the target media resource in the consumption stage; The generation unit 203 is used to generate resource detail information corresponding to the target media resource based on the recommended association data and the user interaction data. The resource detail information is used to display the data corresponding to the complete link of the target media resource from the resource stage to the consumption stage, so as to display the resource detail information through the graphical user interface of the terminal device when the resource detail information is triggered to be displayed.

[0072] In some embodiments, the information processing apparatus includes a processing subunit for: In response to a resource recommendation event for a target business scenario, the target media resource is determined from multiple candidate media resources through a resource recommendation system based on the resource recommendation logic of the target business scenario.

[0073] In some embodiments, the information processing apparatus includes a processing subunit for: The recommendation subsystems are subjected to data tracking operations to collect media resources in each target business scenario and data in each sub-stage of the resource recommendation stage.

[0074] In some embodiments, the information processing apparatus includes a processing subunit for: The collected media resources in each target business scenario, the data in each of the sub-stages of the resource recommendation stage, and the user interaction data generated in the consumption stage after the resource recommendation stage are associated to establish a relationship between the data in each of the sub-stages and the user interaction data.

[0075] In some embodiments, the information processing apparatus includes a processing subunit for: Aggregate and / or accumulate the media resources in each of the target business scenarios and the data in each of the sub-stages of the resource recommendation stage to obtain the traffic data of each media resource in each of the sub-stages. The user interaction data generated in the consumption stage after the resource recommendation stage for each of the target business scenarios is aggregated and / or accumulated to obtain the processed user interaction data of each of the media resources in the consumption stage.

[0076] In some embodiments, the information processing apparatus includes a processing subunit for: Traffic data of each media resource in each sub-stage, and user interaction data after processing of each media resource in the consumption stage, are stored in a preset database.

[0077] In some embodiments, the information processing apparatus includes a processing subunit for: In response to the resource information display event for the target media resource, based on the resource identifier of the target media resource, the traffic data of the target media resource in each sub-stage of the resource recommendation stage is obtained from the preset database and used as the recommendation association data of the target media resource in the resource recommendation stage.

[0078] In some embodiments, the information processing apparatus includes a processing subunit for: In response to a resource information display event for the target media resource, based on the specified time information indicated by the resource information display event and the resource identifier of the target media resource, traffic data of the target media resource in each sub-stage of the resource recommendation stage is obtained from the preset database and used as recommendation association data of the target media resource in the resource recommendation stage.

[0079] In some embodiments, the information processing apparatus includes a processing subunit for: Based on the target display style, the recommended association data, and the user interaction data, resource details information of the target display style corresponding to the target media resource is generated.

[0080] In some embodiments, the target display style includes at least one of card chart style, funnel chart style, table chart style, and trend chart style.

[0081] In one embodiment, the target media resource includes target recommended audio and / or a set of target recommended audio, wherein the set of target recommended audio includes at least two recommended audio tracks.

[0082] This application discloses an information processing device. A first acquisition unit 201 acquires recommendation association data of a target media resource during a resource recommendation stage based on the resource identifier of the target media resource. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage filters media resources entering that sub-stage and outputs them to the next sub-stage for processing until media resources are recommended to the user account during the consumption stage are obtained. The recommendation association data includes traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. A second acquisition unit 202 acquires user interaction data generated by the target media resource in the consumption stage after the recommendation stage based on the resource identifier. The user interaction data includes user interaction behavior with the target media resource during the consumption stage. A generation unit 203 generates resource detail information corresponding to the target media resource based on the recommendation association data and the user interaction data. The resource detail information is used to display the complete link data of the target media resource from the resource stage to the consumption stage, and is displayed through the graphical user interface of the terminal device when the resource detail information is triggered for display. This application embodiment can obtain recommendation association data of the target media resource during the resource recommendation stage, as well as user interaction data generated during the consumption stage of the target media resource after the resource recommendation stage. Based on the recommendation association data and user interaction data, resource detail information corresponding to the target media resource is generated. This resource detail information can be used to intuitively display the traffic data of the target media resource from the resource recommendation stage to the consumption stage, making the traffic data of the media resource from the resource recommendation stage to the consumption stage more transparent. Developers can intuitively observe the traffic conversion of the media resource. Developers can accurately check and optimize the recommendation stage of the media resource based on the resource detail information, thereby effectively improving the recommendation accuracy of the media resource.

[0083] Accordingly, this application also provides an electronic device, which can be a terminal, such as a smartphone, tablet computer, laptop computer, touch screen, game console, personal computer (PC), personal digital assistant (PDA), or other terminal device. Alternatively, the electronic device can be a server.

[0084] like Figure 11 As shown, Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 300 includes a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 and the memory 302 are electrically connected. Those skilled in the art will understand that the electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0085] The processor 301 is the control center of the electronic device 300. It connects various parts of the electronic device 300 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 302, and by calling data stored in the memory 302, it executes various functions and processes data of the electronic device 300, thereby providing overall monitoring of the electronic device 300. The processor 301 can be a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc., and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0086] In this embodiment, the processor 301 in the electronic device 300 loads the instructions corresponding to the processes of one or more applications into the memory 302 according to the following steps, and the processor 301 runs the applications stored in the memory 302 to realize various functions, such as: Based on the resource identifier of the target media resource, the recommendation association data of the target media resource in the resource recommendation stage is obtained. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. Based on the resource identifier, obtain user interaction data generated by the target media resource in the consumption stage after the recommendation stage, wherein the user interaction data includes user interaction behavior with the target media resource during the consumption stage; Based on the recommended association data and the user interaction data, resource details information corresponding to the target media resource is generated. The resource details information is used to display the data corresponding to the complete link of the target media resource from the resource stage to the consumption stage, so as to display the resource details information through the graphical user interface of the terminal device when the resource details information is triggered to be displayed.

[0087] The electronic device provided in this application embodiment can obtain recommendation association data of the target media resource in the resource recommendation stage and user interaction data generated in the consumption stage of the target media resource after the resource recommendation stage. Based on the recommendation association data and user interaction data, it generates resource detail information corresponding to the target media resource. The resource detail information can be used to intuitively display the traffic data of the target media resource from the resource recommendation stage to the consumption stage, making the traffic data of the media resource from the resource recommendation stage to the consumption stage more transparent. Developers can intuitively observe the traffic conversion of the media resource. Developers can accurately check and optimize the recommendation stage of the media resource based on the resource detail information, thereby effectively improving the recommendation accuracy of the media resource.

[0088] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0089] Optional, such as Figure 11 As shown, the electronic device 300 also includes: a touch display screen 303, a radio frequency circuit 304, an audio circuit 305, an input unit 306, and a power supply 307. The processor 301 is electrically connected to the touch display screen 303, the radio frequency circuit 304, the audio circuit 305, the input unit 306, and the power supply 307. Those skilled in the art will understand that... Figure 11 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0090] The touch display screen 303 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The touch display screen 303 may include a display panel and a touch panel. The display panel 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 electronic device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Optionally, the display panel can be configured using a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar technologies. The touch panel can be used to 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 panel), generate corresponding operation commands, and execute the corresponding program according to the operation commands. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, transmitting 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 301. It can also receive and execute commands from the processor 301. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 301 to determine the type of touch event. Subsequently, the processor 301 provides corresponding visual output on the display panel based on the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the touch display screen 303 to achieve input and output functions. However, in some embodiments, the touch panel and the touch display screen 303 can be implemented as two independent components to achieve input and output functions. That is, the touch display screen 303 can also be used as part of the input unit 306 to achieve input functions.

[0091] The radio frequency circuit 304 can be used to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices, and to transmit and receive signals with network devices or other electronic devices.

[0092] Audio circuitry 305 can be used to provide an audio interface between a user and an electronic device via a speaker and a microphone. Audio circuitry 305 converts received audio data into electrical signals, transmits them to the speaker, and the speaker converts them into sound signals for output. Conversely, the microphone converts collected sound signals into electrical signals, which are then received by audio circuitry 305, converted back into audio data, and then processed by processor 301 before being transmitted via radio frequency circuitry 304 to, for example, another electronic device, or output to memory 302 for further processing. Audio circuitry 305 may also include an earphone jack to facilitate communication between peripheral headphones and electronic devices.

[0093] The input unit 306 can be used to receive input numbers, characters, or user characteristic information (such as fingerprints, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.

[0094] Power supply 307 is used to supply power to various components of electronic device 300. Optionally, power supply 307 can be logically connected to processor 301 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. Power supply 307 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0095] although Figure 11 As not shown in the diagram, the electronic device 300 may also include a camera, sensor, wireless fidelity module, Bluetooth module, etc., which will not be described in detail here.

[0096] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0097] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0098] Therefore, embodiments of this application provide a computer-readable storage medium storing multiple computer programs that can be loaded by a processor to execute any of the information processing methods provided in this application. The computer program can execute the steps of the following information processing method: Based on the resource identifier of the target media resource, the recommendation association data of the target media resource in the resource recommendation stage is obtained. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. Based on the resource identifier, obtain user interaction data generated by the target media resource in the consumption stage after the recommendation stage, wherein the user interaction data includes user interaction behavior with the target media resource during the consumption stage; Based on the recommended association data and the user interaction data, resource details information corresponding to the target media resource is generated. The resource details information is used to display the data corresponding to the complete link of the target media resource from the resource stage to the consumption stage, so as to display the resource details information through the graphical user interface of the terminal device when the resource details information is triggered to be displayed.

[0099] Because the computer program stored in this storage medium can obtain recommendation association data of the target media resource during the resource recommendation stage, as well as user interaction data generated during the consumption stage of the target media resource after the resource recommendation stage, it can generate resource detail information corresponding to the target media resource based on the recommendation association data and user interaction data. This allows for a direct display of the traffic data of the target media resource from the resource recommendation stage to the consumption stage, making the traffic data of the media resource from the resource recommendation stage to the consumption stage more transparent. Developers can intuitively observe the traffic conversion of the media resource, and can accurately investigate and optimize the recommendation stage of the media resource based on the resource detail information, thereby effectively improving the recommendation accuracy of the media resource.

[0100] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0101] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0102] Since the computer program stored in the computer-readable storage medium can execute any of the information processing methods provided in the embodiments of this application, it can achieve the beneficial effects that any of the information processing methods provided in the embodiments of this application can achieve, as detailed in the preceding embodiments, and will not be repeated here.

[0103] According to one aspect of this application, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the methods provided in the various optional implementations of the above embodiments.

[0104] In the above embodiments of the information processing apparatus, computer-readable storage medium, electronic device, and computer program product, the descriptions of each embodiment have different focuses. Parts not described in detail in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes and beneficial effects of the information processing apparatus, computer-readable storage medium, computer program product, electronic device, and their corresponding units described above can be referred to the description of the information processing method in the above embodiments, and will not be repeated here.

[0105] The foregoing has provided a detailed description of an information processing method, apparatus, electronic device, computer-readable storage medium, and computer program product provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An information processing method, characterized in that, include: Based on the resource identifier of the target media resource, the recommendation association data of the target media resource in the resource recommendation stage is obtained. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. Based on the resource identifier, obtain user interaction data generated by the target media resource in the consumption stage after the resource recommendation stage, wherein the user interaction data includes user interaction behavior with the target media resource during the consumption stage; Based on the recommended association data and the user interaction data, resource details information corresponding to the target media resource is generated. The resource details information is used to indicate the data corresponding to the complete link of the target media resource from the resource recommendation stage to the consumption stage, so as to display the resource details information through the graphical user interface of the terminal device when the resource details information is triggered to be displayed.

2. The method according to claim 1, characterized in that, Before obtaining the recommendation association data of the target media resource in the resource recommendation stage based on the resource identifier of the target media resource, the method further includes: In response to a resource recommendation event for a target business scenario, the target media resource is determined from multiple candidate media resources through a resource recommendation system based on the resource recommendation logic of the target business scenario.

3. The method according to claim 2, characterized in that, The resource recommendation system includes multiple recommendation subsystems, each of which corresponds to a sub-stage. The method further includes: The recommendation subsystems are subjected to data tracking operations to collect media resources in each target business scenario and data in each sub-stage of the resource recommendation stage.

4. The method according to claim 3, characterized in that, The method further includes: The collected media resources in each target business scenario, the data in each of the sub-stages of the resource recommendation stage, and the user interaction data generated in the consumption stage after the resource recommendation stage are associated to establish a relationship between the data in each of the sub-stages and the user interaction data.

5. The method according to claim 4, characterized in that, The method further includes: Aggregate and / or accumulate the media resources in each of the target business scenarios and the data in each of the sub-stages of the resource recommendation stage to obtain the traffic data of each media resource in each of the sub-stages. The user interaction data generated in the consumption stage after the resource recommendation stage for each of the target business scenarios is aggregated and / or accumulated to obtain the processed user interaction data of each of the media resources in the consumption stage.

6. The method according to claim 5, characterized in that, The method further includes: Traffic data of each media resource in each sub-stage, and user interaction data after processing of each media resource in the consumption stage, are stored in a preset database.

7. The method according to claim 6, characterized in that, The step of obtaining recommendation association data for the target media resource during the resource recommendation stage based on the resource identifier of the target media resource includes: In response to the resource information display event for the target media resource, based on the resource identifier of the target media resource, the traffic data of the target media resource in each sub-stage of the resource recommendation stage is obtained from the preset database and used as the recommendation association data of the target media resource in the resource recommendation stage.

8. The method according to claim 7, characterized in that, In response to a resource information display event for the target media resource, the method involves retrieving traffic data of the target media resource at each sub-stage of the resource recommendation phase from the preset database based on the resource identifier of the target media resource, and using this data as recommendation association data for the target media resource in the resource recommendation phase. This includes: In response to a resource information display event for the target media resource, based on the specified time information indicated by the resource information display event and the resource identifier of the target media resource, traffic data of the target media resource in each sub-stage of the resource recommendation stage is obtained from the preset database and used as recommendation association data of the target media resource in the resource recommendation stage.

9. The method according to any one of claims 1 to 7, characterized in that, The step of generating resource details information corresponding to the target media resource based on the recommended association data and the user interaction data includes: Based on the target display style, the recommended association data, and the user interaction data, resource details information of the target display style corresponding to the target media resource is generated.

10. The method according to claim 9, characterized in that, The target display style includes at least one of the following: card chart style, funnel chart style, table chart style, and trend chart style.

11. The method according to any one of claims 1 to 7, characterized in that, The target media resources include target recommended audio and / or a set of target recommended audio, wherein the set of target recommended audio includes at least two recommended audio files.

12. An information processing device, characterized in that, include: The first acquisition unit is used to acquire recommendation association data of the target media resource in the resource recommendation stage based on the resource identifier of the target media resource. The resource recommendation stage includes multiple sub-stages with a processing order. Each sub-stage is used to filter the media resources entering the sub-stage and output them to the next sub-stage for processing until the media resources recommended to the user account in the consumption stage are obtained. The recommendation association data includes the traffic data of the target media resource in the multiple sub-stages of the resource recommendation stage. The second acquisition unit is used to acquire user interaction data generated by the target media resource in the consumption stage after the recommendation stage, based on the resource identifier, wherein the user interaction data includes user interaction behavior with the target media resource during the consumption stage. The generation unit is used to generate resource detail information corresponding to the target media resource based on the recommended association data and the user interaction data. The resource detail information is used to display the data corresponding to the complete link of the target media resource from the resource stage to the consumption stage, so as to display the resource detail information through the graphical user interface of the terminal device when the resource detail information is triggered to be displayed.

13. An electronic device, characterized in that, The system includes a processor and a memory, the memory storing multiple instructions; the processor loads instructions from the memory to perform the steps of the information processing method as described in any one of claims 1 to 11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a plurality of instructions adapted for loading by a processor to perform the steps of the information processing method as described in any one of claims 1 to 11.

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