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基于二分网络社团划分的推荐算法.
- 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