Explicit User Similarity Scoring for Social Recommendation Relevance
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Solution Overview
Problem
Existing recommendation systems on social networks provide limited insights into how the opinions of others relate to an individual's specific interests or needs, as they are often based on implicit patterns of user behavior without clear indicators of relevance.
Innovation Solution
A system and method that calculates and provides a measure of explicit user similarity between consumers, allowing individuals to understand the degree to which others' opinions are applicable to them, using cosine similarity and inverse user frequency to quantify user interactions and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If recommendation systems use implicit patterns of user behavior, then automation and scalability are improved, but the relevance and applicability of recommendations to individual users deteriorates
Solution Approach 1:
The system implements feedback by explicitly showing users similarity scores that indicate how relevant recommendations from other users are to them. This feedback loop allows users to understand the applicability of recommendations to their specific case, resolving the information loss while maintaining automation
Solution Approach 2:
The patent introduces an intermediary similarity score that mediates between implicit user behavior data and personalized recommendation relevance. This intermediary metric translates automated analysis into meaningful information about individual user applicability
2Productivity
If recommendation systems present average ratings from other viewers, then aggregation of user feedback is improved, but the personalization and applicability to individual users deteriorates
Solution Approach 1:
The system applies local quality by providing different levels of personalized information to different users based on their similarity scores. Instead of uniform average ratings, each user receives tailored similarity metrics that reflect their individual characteristics and needs
Solution Approach 2:
The patent changes the parameter of recommendation presentation from static average ratings to dynamic similarity scores that adapt to each user's profile. This parameter transformation enables both efficient aggregation and individualized adaptability
Data Source
AI summary
A system and method for providing recommendations to individuals on a social network, in which the recommendations include information indicating the similarity of the individuals to one another, to aid the individuals in judging the degree to which the opinions of the others are applicable to the themselves.