Dynamic User Model for Social Media Recommendation Adaptability
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Solution Overview
Problem
Existing social media platforms struggle to dynamically recommend suitable closely related users, as the recommended users are often fixed and difficult to change, leading to unsuitable recommendations.
Innovation Solution
A method and apparatus that acquire and analyze user data on social media to establish a user model, allowing for the discovery of new closely related users by matching third user data with the user model and determining suitability based on a preset value, with options for weighted summation calculation and iterative training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a closely related user is preset and recommended according to user behavior, then the recommendation system can provide initial recommendations, but the recommended users become fixed and difficult to change
Solution Approach 1:
The patent transforms the static recommendation system into a dynamic one by continuously updating the user model with new data. The system periodically recalculates matching degrees between target users and potential closely related users, allowing the composition of recommended users to change over time based on evolving user behaviors and interests, thus resolving the contradiction between stability and adaptability
Solution Approach 2:
The system changes the parameters used for user matching by incorporating multiple dimensions of user data (behavioral data, demographic data, interest data) and dynamically adjusting the weights of different data types. This allows the recommendation system to adapt to changing user characteristics while maintaining a structured approach to user modeling
2Adaptability or versatility
If a fixed closely related user list is used, then the system structure remains simple, but it becomes difficult to discover new closely related users
Solution Approach 1:
The patent segments the user modeling process into distinct modules: data acquisition module, user model establishment module, matching calculation module, and result application module. Each module handles specific aspects of user analysis, making the overall complex system manageable and maintainable while enabling sophisticated user discovery capabilities
Solution Approach 2:
The user model serves as an intermediary between raw user data and recommendation results. This intermediate representation abstracts complex user characteristics into a structured format that can be efficiently compared and matched, reducing the complexity of direct user-user comparison while enabling sophisticated matching algorithms
3Measurement precision
If preset closely related users are recommended, then implementation is straightforward, but the recommendations are not suitable for users
Solution Approach 1:
The system performs preliminary actions by pre-establishing user models and pre-calculating matching criteria before actual recommendation needs arise. User models are built in advance using available data, and matching algorithms are pre-configured with weight parameters, enabling fast and precise recommendations when needed without complex real-time processing
Solution Approach 2:
The system implements feedback mechanisms where user interactions with recommended content are continuously monitored and fed back into the user model. This feedback loop refines the user model over time, improving matching precision by incorporating actual user preferences and behaviors, making the system increasingly accurate while maintaining implementation feasibility
Data Source
AI summary
Embodiments of the present invention provide a method and an apparatus for discovering a closely related user. The method includes: acquiring first data of a first user on social media and second data of a second user on the social media, where the second user is a user following the first user and/or a user followed by the first user; then, establishing a user model of the first user according to the first data and the second data; then, acquiring third data of a third user in social media, where the third user and the first user do not follow each other; afterward, performing matching between the third data and the user model to perform calculation, to obtain a matching result; and finally, if the matching result is greater than a preset value, determining that the third user is a closely related user of the first user.