Genre-Specific Relevance Filtering for Social Network Recommendations
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Solution Overview
Problem
Users of computer-implemented social networks face an overwhelming number of semi-personal recommendations, which can lead to user dissatisfaction and increased network load, as these recommendations often lack relevance due to varying interests between users across different content genres.
Innovation Solution
A method is introduced to calculate a relevance-taste index for each contact-genre tuple based on user reaction behavior, filtering recommendations only if they meet a predetermined criterion, thereby reducing irrelevant suggestions and enhancing user experience by presenting only relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If semi-personal recommendations are automatically generated and presented to users based on contact preferences, then the personalization and trustworthiness of recommendations is improved, but the quantity of recommendations increases overwhelming the user and reducing satisfaction
Solution Approach 1:
The patent applies local quality by determining genre-specific relevance separately for different content genres. Instead of a uniform recommendation approach, the system evaluates relevance independently for each genre (e.g., movies, music, news), allowing personalized recommendations in genres where the contact has relevant expertise while filtering out irrelevant ones. This resolves the contradiction by maintaining personalization quality locally rather than globally across all content types.
Solution Approach 2:
The system changes the parameter of relevance evaluation from a single overall score to multiple genre-specific relevance scores. By introducing genre as a parameter dimension, the system can selectively apply recommendations based on matching the contact's expertise domain with the user's interests in that specific genre, thereby reducing the total number of recommendations while maintaining personalization where applicable.
2Adaptability or versatility
If all semi-personal recommendations from contacts are presented to users, then comprehensive coverage of potential interests is improved, but network load increases due to processing and transmitting large volumes of recommendation data
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing genre-specific relevance scores for each contact-genre tuple before recommendations are generated. When a recommendation request arrives, the system quickly retrieves these pre-computed relevance scores rather than performing complex relevance calculations in real-time. This reduces network load and processing energy while maintaining comprehensive interest coverage, as the filtering decision is made efficiently based on pre-analyzed data.
3Manufacturing precision
If relevance filtering based on contact-genre tuples is applied to recommendations, then recommendation quality and user satisfaction are improved, but system complexity increases due to monitoring and calculating relevance indices
Solution Approach 1:
The patent segments the recommendation filtering system into distinct modular components: (1) a relevance-taste index determination module that analyzes user reactions to recommendations, (2) a genre classification module that categorizes content items, and (3) a filtering module that applies genre-specific relevance thresholds. This segmentation reduces system complexity by making each component independent and manageable, while collectively achieving high recommendation relevance through their coordinated operation.
Data Source
AI summary
The present invention relates to an operating method of operating a recommender system, a filtering apparatus (260) for a recommender system (200), a recommender system and a corresponding computer program. An idea of the invention is to automatically learn for a user A in a social network, which recommendations of contacts of user A, who are also members of the social network, are relevant with respect to a genre into which user A is interested in. A learning algorithm is used to interpret feedback from user A in response to receiving recommendations from his/her contacts. Thereby, for each combination of a contact and a genre, a relevance-taste index can be determined. The determined relevance-taste index is subjected to a filter. Only such recommendations are provided to user A, whose associated relevance-taste indices fulfill a filtering criterion. Thereby, the amount of irrelevant recommendations submitted to user A can be significantly reduced.