A Collaborative Filtering Method using Topological-Potential Based Community Discovery Strategy
Collaborative filtering is one of the most successful technologies for recommender systems. However, it is strongly limited by the sparsity of data. To deal with these limitations, this paper presents an efficient community collaborative filtering method based on community structures. We apply multi-relational data mining techniques to construct a user network, which not only are the user-item ratings utilized but also user and item information, and then adopt a community discovery method based on topological potential to explore community structures, which in turn are used in collaborative filtering. We explore the optimal threshold of relational distance, and compare our method with other collaborative filtering methods. Experimental results show that the proposed community-based method gets lower MAE values and outperforms other methods. And we also find that increasing the size of training users and active users rated items can effectively improve the prediction accuracy.
recommender system collaborative filtering community structure topological potential complex network
Xiyao Chen Chuang Zhang Zhiqing Lin Bo Xiao He Ma
Pattern Recognition & Intelligent System Lab Beijing University of Posts and Telecommunications Beijing, China
国际会议
成都
英文
229-233
2010-12-17（万方平台首次上网日期，不代表论文的发表时间）