User graph regularized pairwise matrix factorization for item recommendation

Abstract. Item recommendation from implicit, positive only feedback is an emerging setup in collaborative filtering in which only one class exam-ples are observed. In this paper, we propose a novel method, called User Graph regularized Pairwise Matrix Factorization (UGPMF), to seam-lessly integrate user information into pairwise matrix factorization pro-cedure. Due to the use of the available information on user side, we are able to find more compact, low dimensional representations for users and items. Experiments on real-world recommendation data sets demonstrate that the proposed method significantly outperforms various competing alternative methods on top-k ranking performance of one-class item rec-ommendation task