Recommendation Algorithm Based on Community Detection in Bipartite Networks

doi:10.12068/j.issn.1005-3026.2018.08.008

Abstract

Abstract: The efficiency of traditional user-based collaborative filtering (user-based CF) recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection (RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly， the user-item rating matrix is mapped into user-item bipartite network. Then， the community information of each user is obtained by using RACD to divide the user-item network. Finally， the items are recommended to the target user according to other users in the same community. Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system.

Key words: recommendation algorithm, bipartite network, community detection, collaborative filtering, complex network

CLC Number:

TP391

CHEN Dong-ming， YAN Yan-bin， HUANG Xin-yu， WANG Dong-qi. Recommendation Algorithm Based on Community Detection in Bipartite Networks[J]. Journal of Northeastern University Natural Science, 2018, 39(8): 1103-1107.

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