Recommendation based on multiple user subgroups

In recent years, the bipartite graph model is becoming popular because of its simplicity and efficiency in recommender systems. However, this model may be ineffective due to the data sparsity and scalability problems. Clustering techniques are effective methods to alleviate these two problems. In this paper we propose a novel method denoted as Multi-US_BG which makes recommendation on the user-item bipartite graph using multiple user subgroups. This method first uses SVD to decompose the rating matrix to get user feature vectors, then utilizes a fuzzy c-means clustering algorithm to cluster users into multiple subgroups. Finally it integrates subgroups with the recommendation method on the user-item bipartite graph. Experimental results on MovieLens show that our method can improve the top-N recommendation performance in Precision, Recall and F1-measure in comparison with the pure recommendation method on the bipartite graph (Pure_BG). Copyright © 2013 Binary Information Press.