1. 基于二分网络社团划分的推荐算法.
- Author
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陈东明, 严燕斌, 黄新宇, and 王冬琦
- Subjects
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BIPARTITE graphs , *FILTERING software , *COMMUNITIES , *ALGORITHMS , *FILTERS & filtration , *THEORY - 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]
- Published
- 2018
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