User Interest Profile Clustering for Social Network Relationship Discovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online communities, such as social networking sites, face limited interaction potential due to inadequate analysis of user activity, resulting in only simple connections being mapped between users.
Innovation Solution
A system and method that automatically generates user-interest profiles by clustering keywords from user log data using natural language parsing and similarity-based clustering algorithms, displaying potential relationships through linking tools, and allowing users to manually modify their profiles, thereby dynamically updating and refining user connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If limited analysis of user activity is used, then system complexity is reduced, but interaction potential between users is limited
Solution Approach 1:
The patent segments user activity analysis into multiple dimensions including interests, skills, demographics, and behaviors. By dividing the analysis into these distinct segments, the system can comprehensively map user relationships without overwhelming complexity, resolving the contradiction between analysis depth and system complexity.
Solution Approach 2:
The patent introduces additional dimensions for user analysis beyond simple connections, including interest groups, skill sets, demographic characteristics, and activity patterns. This multi-dimensional approach expands interaction potential while maintaining manageable system complexity through structured dimensional categorization.
2Device complexity
If simple connections are mapped between users, then system complexity is reduced, but relationship discovery capability is limited
Solution Approach 1:
The patent segments relationship discovery into multiple relationship types including professional connections, shared interests, skill complementarities, and demographic affinities. This segmentation enables precise relationship mapping across different dimensions while keeping the overall system complexity manageable through modular relationship categories.
Solution Approach 2:
The patent changes the parameters of relationship mapping by introducing weighted scoring based on multiple factors such as interest overlap, skill compatibility, demographic similarity, and activity correlation. This parameter-based approach enhances relationship discovery precision while maintaining system complexity at acceptable levels through algorithmic scoring.
3Measurement precision
If comprehensive user activity analysis is performed, then relationship discovery precision is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts only the most significant features from user activity data, such as key interests, primary skills, and major activity patterns, rather than processing all raw data. This extraction approach maintains high relationship analysis precision while significantly reducing the volume of data that requires processing.
Solution Approach 2:
The patent implements partial action by focusing analysis on the most relevant user activity dimensions that contribute to relationship discovery, rather than comprehensively analyzing every aspect of user behavior. This selective approach achieves sufficient precision while minimizing data processing requirements.
Data Source
AI summary
In a system, a similarity based clustering algorithm is executed to generate clusters of user profiles. Each cluster includes a group of users in an electronic community. Each cluster represents a relationship between the users in each group that each cluster includes. Each cluster is stored in a user profile and relationship database. The similarity based clustering algorithm includes s a member importance function and a member similarity function. The member importance function ascertains an importance value of keywords as a depth of the keywords in a semantic hierarchical tree. The member similarity function ascertains a similarity distance between keywords as a path distance between the keywords in the semantic hierarchical tree. Executing the similarity based clustering algorithm includes: using the member importance function and the member similarity function to ascertain the clusters.