Social Context Weighted Content Recommendations
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Solution Overview
Problem
Social networking sites struggle to provide relevant content recommendations due to the proliferation of users and the difficulty in trusting recommendations, often resulting in content that is of little interest to users.
Innovation Solution
An open overlay service system that weights content recommendations based on the social network context of the user, incorporating preferences from family and friends, and uses a network architecture with components like an application server, messaging server, and user database to provide personalized and relevant suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If social networking sites provide content recommendations to all users, then the quantity of recommendations increases, but the relevance and trustworthiness of recommendations decreases
Solution Approach 1:
The patent segments the user base into social network groups (friends, family, acquaintances) and provides different recommendations to each segment based on their specific preferences and relationships. This resolves the contradiction by maintaining high relevance for each user group while providing abundant recommendations overall.
Solution Approach 2:
The system applies local quality by tailoring recommendation content to specific social contexts and user relationships. Recommendations are weighted and filtered based on the local social network context, ensuring high trustworthiness for each user while maintaining system-wide scalability.
2Duration of action of stationary object
If social networking sites use closed platform services to keep users captive, then user retention improves, but user trust and satisfaction with recommendations decreases
Solution Approach 1:
The system implements feedback mechanisms where users can provide input about their preferences and the system learns from their interactions. This creates a trust-building loop that improves recommendation quality over time, maintaining user retention through satisfaction rather than captivity.
Solution Approach 2:
The patent introduces social network relationships as an intermediary layer between the platform and users. Recommendations are filtered and weighted through this social intermediary, which builds trust by leveraging existing user relationships rather than relying on closed platform control.
3Adaptability or versatility
If social networking sites aggregate recommendations from all users, then the diversity of recommendations increases, but the accuracy and personalization of recommendations decreases
Solution Approach 1:
The system adds a social network dimension to the recommendation algorithm, weighting recommendations based on user relationships and social context. This resolves the contradiction by maintaining diversity from multiple users while achieving precision through social-based filtering and weighting.
Solution Approach 2:
The patent creates a composite recommendation system that combines multiple factors: user preferences, social network relationships, content characteristics, and interaction history. This composite approach maintains diversity while achieving accuracy through the synergistic combination of multiple data dimensions.
Data Source
AI summary
Embodiments of the present invention provide users with suggested content that is weighted based on the social network context of the suggestion. In particular, the suggested content is selected based on incorporating the preferences of users having a relationship with the user. For example, content recommendations from a family member or known friend of the user may be highly weighted over other recommendations.