关键词: 重叠社区, 兴趣聚类, 推荐算法, 协同过滤, 矩阵分解

Abstract: Traditional collaborative filtering recommendation algorithms suffer from data sparsity, which results in poor recommendation accuracy. Social connections among users can reflect their interactions, which can be mixed into recommendation algorithms to improve the accuracy. Only straightforward social connections have been used by most current social recommendation algorithms, while users’ latent interest and cluster information haven’t been considered. In response to these circumstances, this paper proposes a collaborative filtering recommendation algorithm combining community structure and interest clusters. Firstly, overlapping community detection algorithm is used to detect the community structure existed in user social network, thus users in the same community have certain common characteristics. Meanwhile, we design a customized fuzzy clustering algorithm to discover users’ interest clusters, which uses item-category relationship and users’ activity history as input. Users in the same cluster are similar in generalized interest. We quantify users’ preference for each social community and interest cluster they belong to respectively. Then, we combine this two types of user group information into matrix factorization model by adding a clustering-based regularization term to improve the objective function. Experiments conducted on the Yelp dataset show that, in comparison to other methods including both traditional and social recommendation algorithms, our approach gets better recommendation results in accuracy.

Key words: overlapping community, interest cluster, recommendation algorithm, collaborative filtering, matrix factorization