Abstract: Collaborative filtering is one of the most successful and widely used techniques among recommender systems.However,it suffers from serious problem in sparsity.Sparsity in ratings makes the formation of neighborhood inaccurate,thereby resulting in poor recommendations.In this paper,bipartite network was used to alleviate the sparsity problem in collaborative filtering.Users and items are mapped to nodes in bipartite network,and resources on items are redistributed.Resource approach degree between items is computed,and the original rating matrix is converted to complete matrix based on the resource approach degree.Then affinity propagation clustering was applied to cluster the ra-ting matrix to improve the scalability of our approach.Finally,two different recommendation methods were presented.One is generating recommendations according to neighbors in the cluster which active user belongs to (BNAPC1),and the other is generating recommendations according to clusters’ preferences (BNAPC2).Experiments on MovieLens and Netflix datasets show that BNAPC1 is more accurate than BNAPC2,and is also superior to existing alternatives.

Key words: Recommender systems,Collaborative filtering,Bipartite network,Affinity propagation clustering