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基于二分网络社团划分的推荐算法.

Authors :
陈东明
严燕斌
黄新宇
王冬琦
Source :
Journal of Northeastern University (Natural Science). 2018, Vol. 39 Issue 8, p1103-1107. 5p.
Publication Year :
2018

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. [ABSTRACT FROM AUTHOR]

Details

Language :
Chinese
ISSN :
10053026
Volume :
39
Issue :
8
Database :
Academic Search Index
Journal :
Journal of Northeastern University (Natural Science)
Publication Type :
Academic Journal
Accession number :
136489597
Full Text :
https://doi.org/10.12068/j.issn.1005-3026.2018.08.008