Behavior-Based Social Recommendation System
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Solution Overview
Problem
Conventional social network connections primarily mirror real-world relationships, leading to noisy and stale suggestions, and fail to effectively recommend users outside of the social network.
Innovation Solution
A method that generates social recommendations by accessing user profile indices to determine reading interests, performing relevance matching, and ranking matching users based on their publishing interests, enabling top-ranked users to be recommended to others.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If social network connections are based on real-world relationships, then the social graph is easy to construct, but the suggestions become noisy and stale
Solution Approach 1:
The patent changes the basis for connection recommendations from static real-world relationship data to dynamic behavior-based interest profiles. By monitoring user behaviors (reading, searching, clicking) and updating interest parameters in real-time, the system generates fresh, relevant connection suggestions that adapt to changing user preferences, resolving the staleness issue while maintaining ease of construction through automated behavioral tracking.
2Ease of operation
If connection suggestions are limited to existing social network users, then the system is simple to operate, but users outside the social network cannot be recommended
Solution Approach 1:
The patent creates a universal recommendation system that functions across multiple domains: it can recommend both existing social network users and external users from partner networks. The behavior-based interest profiling mechanism serves multiple purposes - it works for internal connections and external connections alike, allowing the system to maintain simplicity while expanding versatility to include users outside the original social network.
3Reliability
If behavior tracking is implemented to improve recommendation accuracy, then suggestion quality improves, but system complexity increases
Solution Approach 1:
The system implements self-service through automated behavioral tracking and interest profile generation. Instead of requiring manual user input or complex configuration, the system automatically monitors user behaviors (reading articles, searching, clicking), extracts interest signals, updates profiles, and generates recommendations autonomously. This automation reduces the perceived complexity for users while maintaining high recommendation accuracy through continuous behavioral analysis.
Data Source
AI summary
A process for generating social recommendations is provided. For each user, a user profile index is accessed to determine reading interests of the user. Further, relevance matching is performed to determine matching users having at least one publishing interest that is relevant to the reading interests of the user. Next, the matching users are ranked. Based on the ranking, one or more top ranked matching user(s) are determined. Additionally, a social recommendation for each of the top ranked matching user(s) is enabled to be made to the user.