Social Network Authority Ranking via Topic Group Analysis
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Solution Overview
Problem
Social networks face limitations in providing relevant services to users who do not provide direct information about themselves, hindering their ability to identify user expertise and offer personalized recommendations.
Innovation Solution
A method and system for identifying authorities on expertise topics by analyzing topic groups, determining frequent accounts within these groups, and generating a ranking of authoritative accounts, which utilizes a social network platform to connect client devices and process account data to provide personalized recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users do not provide direct information about themselves, then the social network's ability to provide personalized services is limited, but users may prefer not to share personal information for privacy reasons
Solution Approach 1:
The patent uses topic groups as intermediaries to infer user expertise without requiring direct user input. By analyzing which topic groups users join and their interaction patterns within these groups, the system indirectly determines user expertise and provides personalized recommendations without users explicitly sharing personal information.
Solution Approach 2:
The patent replaces the mechanical approach of directly collecting user-provided information with an automated analysis system that processes user behavior data, topic group memberships, and interaction patterns to infer expertise automatically through computational methods.
2Adaptability or versatility
If the social network analyzes user behavior and topic group memberships to identify expertise, then personalized recommendations can be provided, but the complexity of data processing increases
Solution Approach 1:
The patent segments the expertise identification process into distinct stages: collecting topic group memberships, analyzing interaction patterns within groups, determining frequent accounts, and generating expertise rankings. This segmentation allows the complex analysis to be broken down into manageable computational steps that can be processed efficiently.
Solution Approach 2:
The patent performs preliminary actions by pre-organizing user data into topic group memberships and interaction records before analysis. By structuring the data in advance and identifying frequent accounts ahead of time, the system reduces the computational burden during the actual expertise determination process.
3Measurement precision
If the system processes more topic groups to identify authorities, then the accuracy of expertise identification improves, but the time and computational resources required increase
Solution Approach 1:
The patent applies partial action by processing topic groups selectively rather than exhaustively. It identifies a sufficient number of topic groups and frequent accounts needed to achieve accurate expertise identification without processing every possible topic group, thereby balancing accuracy with processing efficiency.
Solution Approach 2:
The patent skips unnecessary processing steps by directly analyzing topic group memberships and interaction patterns to identify frequent accounts, rather than examining every user interaction in detail. This allows the system to rapidly process large volumes of data while maintaining identification accuracy.
Data Source
AI summary
A method involving obtaining a first plurality of topic groups (TGs), each having a membership of accounts, identifying a first plurality of accounts as authorities for an expertise topic, obtaining a second plurality of TGs with a number of accounts as members, wherein the first plurality of TGs comprises the second plurality of TGs, identifying a first frequent account which is a member in at least one of the second plurality of TGs, adding the first frequent account to the authorities of the expertise topic to obtain a second plurality of accounts as the authorities of the expertise topic, determining a third plurality of TGs in which a second number of accounts from the second plurality of accounts are members, determining that another frequent account is a member in one of the third plurality of TGs, and obtaining a ranking of accounts that are an authority on the expertise topic.