An explicit trust and distrust clustering based collaborative filtering recommendation approach

Clustering based recommender systems have been demonstrated to be efficient and scalable to large-scale datasets. However, due to the employment of dimensionality reduction techniques, clustering based recommendation approaches generally suffer from relatively low accuracy and coverage. To tackle these problems, some trust clustering based recommendation methods are proposed which cluster the social trust information other than the user-item ratings. Existing trust clustering based recommendation algorithms only consider trust relationships, regardless of the distrust information. In addition, these methods simply perform traditional collaborative filtering method in the detected trust communities, which cannot handle the data sparsity and cold start problems effectively. In order to solve these issues, in this paper, an explicit trust and distrust clustering based collaborative filtering recommendation method is proposed. Firstly, a SVD signs based clustering algorithm is proposed to process the trust and distrust relationship matrix in order to discover the trust communities. Secondly, a sparse rating complement algorithm is proposed to generate dense user rating profiles which alleviates the sparsity and cold start problems to a very large extent. Finally, the prediction of missing ratings can be obtained by combining the newly generated user rating profiles and the traditional collaborative filtering algorithm. Experimental results on real-world dataset demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation.