ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (8): 1664-1672.doi: 10.7544/issn1000-1239.2016.20160175

Special Issue: 2016数据挖掘前沿技术专题

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Collaborative Filtering Recommendation Algorithm Combining Community Structure and Interest Clusters

Guo Hongyi, Liu Gongshen, Su Bo,Meng Kui   

  1. (Shool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240)
  • Online:2016-08-01

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

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