Vectorized Semantic Community Detection and Merging Based on User Preferences
JIN Jieliang
SUN Meili
YANG Shan
Abstract：Community detection has become a crucial research area in personalized recommendation.However,existing methods often overlook the effective integration of user preferences with community structures,as well as the recommendation bias introduced by community redundancy.To address these issues,we propose a method for vectorized semantic community detection and merging based on user preferences.First,a preference knowledge base is constructed to capture user preferences.Second,seed nodes are identified using the preference knowledge base and the LeaderRank algorithm.Finally,to examine he changing trends in the shared themes and compactness of communities before and after merging,communities with high redundancy are merged based on vectorized themes and keywords.Through parameter and comparative experiments,we demonstrate that the proposed method outperforms mainstream community detection techniques,which can enhance the accuracy of community-based recommendations and improve the user experience.
Keywords：community detectionpersonalized recommendationcommunity redundancyuser preferences
Publication Date：2024-11-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 58-64 )