Dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavior data

Abstract

This invention discloses a dynamic, intelligent, multi-module real-time content recommendation system based on multi-channel behavioral data. It includes a basic tools module, a creation tools module, a content stream module, a creation intelligent agent module, a multi-channel data collection and analysis system, and a cross-module dynamic linkage recommendation module. This invention can accurately predict user needs and proactively locate relevant content, significantly reducing user browsing and searching time, improving the matching accuracy of the first-screen content, and enhancing content acquisition efficiency. Furthermore, it can dynamically adjust the content display strategy and functional arrangement order of each module based on user behavior, breaking down barriers between modules and achieving intelligent linkage between multiple modules. This provides users with a more personalized creation experience and effectively solves the core problems of content homogenization, module isolation, and poor user experience faced by current AI content creation platforms. It provides users with a dynamic, intelligent, multi-module real-time content recommendation system based on multi-channel behavioral data.

Description

Technical Field

[0001] This invention relates to the field of content recommendation system technology, and in particular to a dynamic, intelligent, multi-module real-time content recommendation system based on multi-channel behavioral data. Background Technology

[0002] In the current digital content creation and consumption environment, users face the dual challenges of information overload and inefficient acquisition. Traditional content recommendation systems typically employ fixed algorithm models, lacking the ability to respond in real-time to comprehensive user behavior and preferences, resulting in low recommendation accuracy and fragmented user experience. While existing AI-generated content platforms possess powerful content generation capabilities, they still exhibit significant shortcomings in content display and user interaction. In summary, the following problems exist: 1. Homogeneous content flow design: Mainstream AI-generated platforms generally adopt dual-stream or multi-stream content display formats, lacking innovation and differentiation, leading to a homogenized user experience and failing to meet the personalized needs of different users; 2. Isolated operation of functional modules: The content information flow lacks organic connection with other functional modules of the product, operating independently and failing to form a synergistic effect, thus reducing the overall user efficiency; 3. Low content acquisition efficiency: Traditional recommendation systems cannot achieve accurate recommendations on the first or second screen, causing user churn in the early stages of browsing, making it difficult to maintain user attention and engagement. Users typically need to flip through pages multiple times or search frequently to find the content they need, significantly reducing the user experience.

[0003] In summary, this application proposes a dynamic, intelligent, multi-module real-time content recommendation system based on multi-channel behavioral data. Summary of the Invention

[0004] Based on the technical problems existing in the background technology, this invention proposes a dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data.

[0005] The present invention proposes a dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data, which includes a basic tool module, a creation tool module, a content flow module, a creation intelligent agent module, a multi-channel data collection and analysis system, and a cross-module dynamic linkage recommendation module. The multi-channel data collection and analysis system includes a multi-channel behavioral data collection module and a behavioral analysis and modeling module.

[0006] Preferably, the basic tools module is located at the top of the product, providing a set of basic functions required for creation, including basic image processing editing functions such as image editing, background erasure, high-definition repair, and lossless magnification.

[0007] Preferably, the creation tool module is used to provide multiple types of image or video creation functions according to different application scenarios, and it is divided into the following different categories according to the usage scenario:

[0008] Lifestyle category: Includes tools for creating portrait photos, couple photos, pet photos, social media avatars, and everyday life-related content;

[0009] Job-related: Includes professional creation tools for e-commerce background images, e-commerce materials, logo creation, and tattoo creation;

[0010] Popular Category: Recommends currently trending gameplay and user-preferred content.

[0011] Preferably, the content stream module adopts a vertical sliding layout with a quick location bar at the bottom, enabling users to quickly locate the content area of ​​interest. It is used to display recommended content and dynamically adjust the sorting and display strategy based on user behavior.

[0012] Preferably, the creative agent module exists in the form of a dialogue interface, through which users can express their creative needs and receive user instructions based on the dialogue format. The agent can recommend suitable scenarios or complete the raw image / video task directly in the dialog box. The agent can proactively provide creative suggestions, tool recommendations, and operation guidance.

[0013] Preferably, the multi-channel behavior data collection module is used to collect user behavior data in the basic tool module, creation tool module, content flow module and creation intelligent agent module, which specifically includes user function usage frequency and order, creation tool preference, content browsing trajectory, dwell time, operation preference, intelligent agent interaction record and instruction preference, cross-module operation path and temporal relationship, and the content style characteristics preferred by the user.

[0014] The behavior analysis and modeling module constructs user behavior profiles and preference models through multi-dimensional data fusion analysis, and updates model parameters in real time to ensure that the recommendation system can dynamically respond to changes in user needs. The specific logical steps are as follows:

[0015] S101: Extract structured behavioral features from the raw behavioral data collected by the multi-channel behavioral data acquisition module to form a multi-dimensional vector. It includes Recent activity levels of basic tools Distribution of creative preferences by genre Average duration of content stream dwell time User activity time period distribution Recent frequency of keywords in AI agent dialogues The frequency of content tags associated with click behavior is used to normalize and time-weight the behavior feature vector, resulting in... = ;

[0016] S102: Use a clustering algorithm (K-means) to form a behavioral profile label for each user's behavioral feature vector, representing the content preference group to which the user belongs. The behavioral profile label is used as one of the prior inputs for the subsequent recommendation model. The behavioral profile labels include e-commerce oriented users, creative experiment users, tool efficiency users, and beginner guidance users.

[0017] S103: Construct a user-content rating function and train a recommendation scoring model. The formula is as follows: ,in The weights of user feature dimension i, Assess the relevance of the user to the content in the i-th dimension (e.g., the degree of match between clicks, likes, favorites, dwell time behavior and content tags). The matching score for user U and content c;

[0018] S104: Dynamic Parameter Update and Adaptive Learning: After each new user action, the following logic is executed:

[0019] (1) Update the user behavior vector using the following formula: , where γ∈[0,1] is the historical weight coefficient;

[0020] (2) Adjust the distribution of behavioral weights Increase the weight of recent frequent behaviors in recommendations;

[0021] (3) Adjust the recommendation ranking in real time and use behavioral feedback (clicks, exits, etc.) as negative / positive sampling signals to optimize the recommendation model;

[0022] (4) When a user’s behavior significantly deviates from the group center, update their profile category and trigger the cross-module dynamic linkage recommendation module to perform actions;

[0023] S105: In any module, if the user behavior vector undergoes a significant shift, the following condition is met: This will trigger the cross-module dynamic linkage recommendation module to perform actions.

[0024] Preferably, the cross-module dynamic linkage recommendation module dynamically adjusts the content and function recommendations based on the user's current behavior to achieve real-time linkage between various functional modules. The specific logical steps are as follows:

[0025] S201: Events collected by the multi-channel behavior data collection module are encapsulated into behavior event tuples: ,in For the module where the current action occurs, For operation behavior type, These are behavioral parameters, including time, content tags, and interactive words;

[0026] S202: Through the event handling engine Real-time semantic analysis is performed to identify the potential intent of the user's current operation. For example, if a user frequently uses "background erase" + "high resolution repair" → it is inferred that their goal is e-commerce image optimization; if a user continuously browses "social avatar" type images in the content stream → it is inferred that their interest leans towards social scene creation, and behavioral offset is calculated by combining the user profile model and historical behavior path: , This is considered a "mutation of behavior pattern," triggering dynamic cross-module linkage;

[0027] S203: Based on behavioral intent and module dependencies, determine the set of target modules that need to be linked: And sort them by priority;

[0028] S204: For each linked module The recommended strategy is updated, and the formula used in the dynamic adjustment process is as follows: ,in To trigger the response strength parameter, The value is 1 when the indicator function is triggered and the behavior offset is significant.

[0029] S205: Once the linkage recommendation strategy takes effect, the UI interface will immediately rearrange the display order of modules, and the linked modules will quickly refresh their content or functions. If there is no user interaction delay, the current display rhythm will be maintained.

[0030] S206: The system records the response effect of each linked recommendation, including whether the user clicked on the recommended content, whether the dwell time increased after the linkage, and whether the next action was triggered. The system updates the user model parameters based on this feedback data. Content matching rules and related recommendation strategies This forms a self-learning closed loop of collaborative recommendations.

[0031] Preferably, in step S204, the content of the recommendation strategy update is as follows:

[0032] (1) Content Flow Module: Adaptive Changes in Content Flow Layout: Records long-term user preferences and automatically locates the user's frequently accessed preferred sections each time the user logs in. It also dynamically adjusts the default sorting method, content density, and display format of the content flow based on the user's usage habits. Personalized Adjustment of Content Flow Image Layout: Automatically adjusts the image layout in the content flow based on the user's preferred style. The content templates of the preferred style are visually enhanced and can be dynamically adjusted according to the user's browsing habits.

[0033] (2) Adaptive layout of functional modules: The layout of the basic tool module and the creative tool module is dynamically adjusted according to the user's usage frequency. Frequently used functions are automatically displayed in front, while functions with low usage frequency are automatically collapsed to save interface space and improve user experience. At the same time, it supports customizable sorting of functional modules, and the system provides default sorting suggestions based on user habits.

[0034] (3) Dynamic adaptation of the agent's persona: The system analyzes the user's interaction style and response preferences, and intelligently adjusts the AI ​​Agent's communication style and persona characteristics. For efficiency-oriented users, the agent module adopts a simple and efficient communication method, reduces redundant hierarchical guidance, and provides solutions directly. For creativity-oriented users, the agent module displays more imaginative and humorous persona characteristics. For learning-oriented users, the agent actively provides more tutorials and guidance information.

[0035] (4) Multi-device synchronization and scene continuation: User preferences and settings can be synchronized across devices to ensure a consistent personalized experience on different terminal devices. It supports cross-device continuation of creative tasks. The system automatically records the creative progress and environmental parameters. It provides intelligent recommendations based on time and scene, prioritizing professional content during working hours and favoring entertainment creation during leisure time.

[0036] (5) Evolution of interaction mode: Analyze user operation habits, automatically adjust the response priority of interaction mode, dynamically adjust the frequency and level of operation prompts according to user proficiency, and support custom interaction process. The system can learn the user's unique operation habits and optimize the response mode.

[0037] Compared with existing technologies, the beneficial effects of this invention are:

[0038] 1. It can accurately predict user needs and proactively locate relevant content, significantly reducing the time users spend browsing and searching, improving the matching degree of the first screen content, improving the efficiency of content acquisition, and reducing user churn rate;

[0039] 2. By setting up a cross-module dynamic linkage recommendation module, the content display strategy and function arrangement order of each functional module can be dynamically adjusted according to user behavior, breaking down the barriers between modules and realizing intelligent linkage between modules. This makes the various parts of the product form an organic whole, breaking through the homogeneity limitations of traditional AI content platforms. Through an innovative multi-module linkage mechanism, a unique product differentiation advantage is established, providing users with a more personalized creation experience. In addition, by continuously learning user behavior patterns, it can gradually adjust to a form that is more in line with users' personal habits, providing truly personalized services. Furthermore, through intelligent guidance and accurate recommendations, it enhances users' trust and dependence on the product, extends usage time, and improves user retention.

[0040] This invention can accurately predict user needs and proactively locate relevant content, significantly reducing user browsing and searching time, improving the matching degree of content on the first screen, and enhancing content acquisition efficiency. Furthermore, it can dynamically adjust the content display strategy and function arrangement order of each functional module based on user behavior, breaking down barriers between modules and achieving intelligent linkage between multiple modules. This provides users with a more personalized creation experience and effectively solves the core problems of content homogenization, module isolation, and poor user experience faced by current AI content creation platforms. It provides users with a dynamic, intelligent, multi-module real-time content recommendation system based on multi-channel behavioral data, achieving a truly intelligent, personalized, and highly efficient creation and content consumption experience. Attached Figure Description

[0041] Figure 1 This is a block diagram of the dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data proposed in this invention;

[0042] Figure 2 This is a flowchart of the dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data proposed in this invention. Detailed Implementation

[0043] The present invention will be further explained below with reference to specific embodiments.

[0044] Example

[0045] Reference Figure 1-2 This embodiment proposes a dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data, including a basic tool module, a creation tool module, a content flow module, a creation intelligent agent module, a multi-channel data collection and analysis system, and a cross-module dynamic linkage recommendation module. The multi-channel data collection and analysis system includes a multi-channel behavioral data collection module and a behavioral analysis and modeling module.

[0046] The basic tools module is located at the top of the product and provides a set of basic functions needed for creation, including basic image processing editing functions such as image editing, background erasure, high-definition repair and lossless enlargement.

[0047] The creation tools module provides various image or video creation functions for different application scenarios, and it is divided into the following categories according to the usage scenario:

[0048] Lifestyle category: Includes tools for creating portrait photos, couple photos, pet photos, social media avatars, and everyday life-related content;

[0049] Job-related: Includes professional creation tools for e-commerce background images, e-commerce materials, logo creation, and tattoo creation;

[0050] Popular category: Recommends currently trending gameplay and user preference-related content;

[0051] The content feed module adopts a vertical sliding layout with a quick navigation bar at the bottom, allowing users to quickly locate the content area of ​​interest. It is used to display recommended content and dynamically adjusts the sorting and display strategy based on user behavior.

[0052] The creative agent module exists in the form of a dialogue interface. Users can express their creative needs through dialogue and receive user instructions based on the dialogue. The agent can recommend suitable scenarios or complete the raw image / video task directly in the dialog box. The agent can proactively provide creative suggestions, tool recommendations and operation guidance.

[0053] The multi-channel behavior data collection module is used to collect user behavior data in the basic tools module, creation tools module, content flow module, and creation agent module. Specifically, it includes the frequency and order of user function usage, creation tool preferences, content browsing trajectory, dwell time, operation preferences, agent interaction records and command preferences, cross-module operation paths and temporal relationships, and the content style characteristics preferred by users.

[0054] The behavior analysis and modeling module constructs user behavior profiles and preference models through multi-dimensional data fusion analysis, and updates model parameters in real time to ensure that the recommendation system can dynamically respond to changes in user needs. The specific logical steps are as follows:

[0055] S101: Extract structured behavioral features from the raw behavioral data collected by the multi-channel behavioral data acquisition module to form a multi-dimensional vector. It includes Recent activity levels of basic tools Distribution of creative preferences by genre Average duration of content stream dwell time User activity time period distribution Recent frequency of keywords in AI agent dialogues The frequency of content tags associated with click behavior is used to normalize and time-weight the behavior feature vector, resulting in... = ;

[0056] S102: Use a clustering algorithm (K-means) to form a behavioral profile label for each user's behavioral feature vector, representing the content preference group to which the user belongs. The behavioral profile label is used as one of the prior inputs for the subsequent recommendation model. The behavioral profile labels include e-commerce oriented users, creative experiment users, tool efficiency users, and beginner guidance users.

[0057] S103: Construct a user-content rating function and train a recommendation scoring model. The formula is as follows: ,in The weights of user feature dimension i, Assess the relevance of the user to the content in the i-th dimension (e.g., the degree of match between clicks, likes, favorites, dwell time behavior and content tags). The matching score for user U and content c;

[0058] S104: Dynamic Parameter Update and Adaptive Learning: After each new user action, the following logic is executed:

[0059] (1) Update the user behavior vector using the following formula: , where γ∈[0,1] is the historical weight coefficient;

[0060] (2) Adjust the distribution of behavioral weights Increase the weight of recent frequent behaviors in recommendations;

[0061] (3) Adjust the recommendation ranking in real time and use behavioral feedback (clicks, exits, etc.) as negative / positive sampling signals to optimize the recommendation model;

[0062] (4) When a user’s behavior significantly deviates from the group center, update their profile category and trigger the cross-module dynamic linkage recommendation module to perform actions;

[0063] S105: In any module, if the user behavior vector undergoes a significant shift, the following condition is met: This triggers the cross-module dynamic linkage recommendation module to execute actions;

[0064] The cross-module dynamic linkage recommendation module dynamically adjusts content and function recommendations based on the user's current behavior, enabling real-time linkage between various functional modules. The specific logical steps are as follows:

[0065] S201: Events collected by the multi-channel behavior data collection module are encapsulated into behavior event tuples: ,in For the module where the current action occurs, For operation behavior type, These are behavioral parameters, including time, content tags, and interactive words;

[0066] S202: Through the event handling engine Real-time semantic analysis is performed to identify the potential intent of the user's current operation. For example, if a user frequently uses "background erase" + "high resolution repair" → it is inferred that their goal is e-commerce image optimization; if a user continuously browses "social avatar" type images in the content stream → it is inferred that their interest leans towards social scene creation, and behavioral offset is calculated by combining the user profile model and historical behavior path: , This is considered a "mutation of behavior pattern," triggering dynamic cross-module linkage;

[0067] S203: Based on behavioral intent and module dependencies, determine the set of target modules that need to be linked: And sort them by priority;

[0068] S204: For each linked module The recommended strategy is updated, and the formula used in the dynamic adjustment process is as follows: ,in To trigger the response strength parameter, The value is 1 when the indicator function is triggered and the behavior offset is significant.

[0069] The content of the recommendation strategy update is as follows:

[0070] (1) Content Flow Module: Adaptive Changes in Content Flow Layout: Records long-term user preferences and automatically locates the user's frequently accessed preferred sections each time the user logs in. It also dynamically adjusts the default sorting method, content density, and display format of the content flow based on the user's usage habits. Personalized Adjustment of Content Flow Image Layout: Automatically adjusts the image layout in the content flow based on the user's preferred style. The content templates of the preferred style are visually enhanced and can be dynamically adjusted according to the user's browsing habits.

[0071] (2) Adaptive layout of functional modules: The layout of the basic tool module and the creative tool module is dynamically adjusted according to the user's usage frequency. Frequently used functions are automatically displayed in front, while functions with low usage frequency are automatically collapsed to save interface space and improve user experience. At the same time, it supports customizable sorting of functional modules, and the system provides default sorting suggestions based on user habits.

[0072] (3) Dynamic adaptation of the agent's persona: The system analyzes the user's interaction style and response preferences, and intelligently adjusts the AI ​​Agent's communication style and persona characteristics. For efficiency-oriented users, the agent module adopts a simple and efficient communication method, reduces redundant hierarchical guidance, and provides solutions directly. For creativity-oriented users, the agent module displays more imaginative and humorous persona characteristics. For learning-oriented users, the agent actively provides more tutorials and guidance information.

[0073] (4) Multi-device synchronization and scene continuation: User preferences and settings can be synchronized across devices to ensure a consistent personalized experience on different terminal devices. It supports cross-device continuation of creative tasks. The system automatically records the creative progress and environmental parameters. It provides intelligent recommendations based on time and scene, prioritizing professional content during working hours and favoring entertainment creation during leisure time.

[0074] (5) Evolution of interaction mode: Analyze user operation habits, automatically adjust the response priority of interaction mode, dynamically adjust the frequency and level of operation prompts according to user proficiency, and support custom interaction flow. The system can learn the user's unique operation habits and optimize the response mode.

[0075] S205: Once the linkage recommendation strategy takes effect, the UI interface will immediately rearrange the display order of modules, and the linked modules will quickly refresh their content or functions. If there is no user interaction delay, the current display rhythm will be maintained.

[0076] S206: The system records the response effect of each linked recommendation, including whether the user clicked on the recommended content, whether the dwell time increased after the linkage, and whether the next action was triggered. The system updates the user model parameters based on this feedback data. Content matching rules and related recommendation strategies This forms a self-learning closed loop for collaborative recommendations;

[0077] This embodiment can accurately predict user needs and proactively locate relevant content, significantly reducing user browsing and searching time, improving the matching degree of the first screen content, and enhancing content acquisition efficiency. It can also dynamically adjust the content display strategy and function arrangement order of each functional module based on user behavior, breaking down barriers between modules and realizing intelligent linkage between multiple modules. This provides users with a more personalized creation experience and effectively solves the core problems of content homogenization, module isolation, and poor user experience faced by current AI content creation platforms. It provides users with a dynamic, intelligent, multi-module real-time content recommendation system based on multi-channel behavioral data, achieving a truly intelligent, personalized, and efficient creation and content consumption experience.

[0078] In this embodiment, during user operations, the multi-channel behavior data acquisition module collects real-time behavioral data from the user in the basic tool module (such as background erasing and high-definition restoration), creation tool module (such as e-commerce templates and social avatars), content stream module (such as browsing, dwelling, and liking), and creation intelligent agent module (such as command input and dialogue feedback). The behavior analysis and modeling module then constructs a behavior feature vector containing dimensions such as operation sequence, usage frequency, content preference, and creation style. Subsequently, the behavior analysis and modeling module performs intent recognition and user profile construction based on this vector, and predicts the user's current behavior using clustering algorithms and weighted scoring functions. The system anticipates potential creative needs; when it detects a significant deviation between user behavior and historical patterns, it triggers a cross-module dynamic linkage recommendation module. This module dynamically adjusts the content display strategy and function arrangement order of each functional module based on the behavior trigger, such as automatically jumping to content flow sections, displaying specific tools in advance, and activating personalized suggestions from the intelligent agent. The recommendation score is adjusted using time weighting and continuously updated based on user clicks and feedback data, achieving millisecond-level response to user operations and multi-module collaborative recommendations. Ultimately, this results in a highly personalized content and function recommendation process, improving creation efficiency and system user experience.

[0079] For example:

[0080] Users can select and use the "Background Eraser" function in the basic tools module to process a product image;

[0081] The system analyzes the operation characteristics in real time and combines them with historical usage data to determine that the user may have optimization needs related to e-commerce. This immediately triggers a cross-module dynamic linkage recommendation module, specifically:

[0082] (1) The creation tools module automatically adjusts the displayed content, and displays e-commerce related tools (such as e-commerce background images and e-commerce materials) in the "work" category in front, so as to improve the efficiency of users in discovering related tools;

[0083] (2) If the user further selects e-commerce related tools, the system will increase the predictive weight of e-commerce-related needs;

[0084] (3) The content flow module responds synchronously. When the user scrolls down to browse the content, the system automatically positions the content to the e-commerce related content area of ​​the "Work" section, and the "Work" option is highlighted in the bottom positioning bar accordingly.

[0085] (4) The intelligent agent module senses the user's behavior path and actively pops up prompts to recommend optimization suggestions for e-commerce images, such as "You seem to be processing e-commerce images. Do you need some background templates suitable for e-commerce?" or "I can help you directly generate product display images that conform to e-commerce specifications."

[0086] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A dynamic, intelligent, multi-module real-time content recommendation system based on multi-channel behavioral data, characterized in that: It includes a basic tools module, a creation tools module, a content flow module, a creation intelligent agent module, a multi-channel data collection and analysis system, and a cross-module dynamic linkage recommendation module. The multi-channel data collection and analysis system includes a multi-channel behavioral data collection module and a behavioral analysis and modeling module. The behavior analysis and modeling module constructs user behavior profiles and preference models through multi-dimensional data fusion analysis, and updates model parameters in real time to ensure that the recommendation system can dynamically respond to changes in user needs. The specific logical steps are as follows: S101: Extract structured behavioral features from the raw behavioral data collected by the multi-channel behavioral data collection module to form a user behavior feature vector. It includes Recent activity levels of basic tools Distribution of creative preferences by genre Average duration of content stream dwell time User activity time period distribution, frequency of keywords in recent agent dialogues Click behavior content tag frequency, user behavior feature vector After normalization and time-weighted processing, we obtain = ; S102: For each user behavior feature vector The K-means clustering algorithm is used to form behavioral profile labels, which represent the content preference groups to which users belong. These behavioral profile labels serve as one of the prior inputs for the subsequent recommendation model. The behavioral profile labels include e-commerce-oriented users, creative experimentation users, tool efficiency-oriented users, and beginner guidance-oriented users. S103: Construct a user-content rating function and train a recommendation scoring model. The formula is as follows: ,in The weights of user feature dimension i, Score the relevance of user a to content c in the i-th dimension. The matching score for user a to content c; S104: Dynamic Parameter Update and Adaptive Learning: After each new user action, the following logic is executed: (1) Update user behavior feature vector The formula used is: , where γ∈[0,1] is the historical weight coefficient; (2) Adjusting weights Increase the weight of recent frequent behaviors in recommendations; (3) Adjust the recommendation ranking in real time and use the behavioral feedback as a negative / positive sampling signal to optimize the recommendation model; (4) When a user’s behavior significantly deviates from the group center, update their profile category and trigger the cross-module dynamic linkage recommendation module to perform actions; S105: In any module, if the user behavior feature vector A significant shift occurs, satisfying This triggers the cross-module dynamic linkage recommendation module to execute actions; The cross-module dynamic linkage recommendation module dynamically adjusts the content and function recommendations based on the user's current behavior, realizing real-time linkage between various functional modules. Its specific logical steps are as follows: S201: Events collected by the multi-channel behavior data collection module are encapsulated into behavior event tuples: ,in For the module where the current action occurs, For operation behavior type, These are behavioral parameters, including time, content tags, and interactive words; S202: Through the event handling engine Perform real-time semantic analysis to identify the potential intent of the user's current action, and calculate the behavior offset by combining the user profile model with historical behavior paths: , If this occurs, it is considered a "mutation of behavior pattern" and triggers dynamic cross-module linkage; S203: Based on behavioral intent and module dependencies, determine the set of target modules that need to be linked: And sort them by priority; S204: For each linked module The recommended strategy is updated, and the formula used in the dynamic adjustment process is as follows: ,in To trigger the response strength parameter, The value is 1 when the indicator function is triggered and the behavior offset is significant. S205: Once the linkage recommendation strategy takes effect, the UI interface will immediately rearrange the display order of modules, and the linked modules will quickly refresh their content or functions. If there is no user interaction delay, the current display rhythm will be maintained. S206: The system records the response effect of each linked recommendation, including whether the user clicked on the recommended content, whether the dwell time increased after the linkage, and whether the next action was triggered. The system updates the user behavior feature vector based on this feedback data. Correlation score and the set of target modules that need to be linked. This forms a self-learning closed loop of collaborative recommendations.

2. The dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data according to claim 1, characterized in that, The basic tools module is located at the top of the product and provides a set of basic functions required for creation, including basic image processing editing functions such as image editing, background erasure, high-definition repair, and lossless enlargement.

3. The dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data according to claim 1, characterized in that, The content stream module adopts a vertical sliding layout with a quick location bar at the bottom, allowing users to quickly locate the content area of ​​interest. It is used to display recommended content and dynamically adjusts the sorting and display strategy based on user behavior.

4. The dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data according to claim 1, characterized in that, The creative agent module exists in the form of a dialogue interface. Users can express their creative needs through dialogue and receive user instructions based on the dialogue format. The agent can recommend suitable scenarios or complete the raw image / video task directly in the dialog box. The agent can proactively provide creative suggestions, tool recommendations, and operation guidance.

5. The dynamic intelligent multi-module real-time content recommendation system based on multi-channel behavioral data according to claim 1, characterized in that, The multi-channel behavior data collection module is used to collect user behavior data in the basic tools module, creation tools module, content flow module, and creation intelligent agent module. Specifically, it includes the frequency and order of user function usage, creation tools preference, content browsing trajectory, dwell time, operation preference, intelligent agent interaction records and command preference, cross-module operation path and temporal relationship, and the content style characteristics preferred by the user.

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