Social network regularized Sparse Linear Model for Top-N recommendation

Social recommendation techniques have been developed to employ user's social connections for both rating prediction and Top-N recommendation. However, they are mostly using social network enhanced matrix factorization (MF) where the objective is to minimize the prediction error of rating scores, which makes it impractical and unsuccessful for Top-N recommendation. This paper thus focuses on developing more effective methods to utilize social network information for Top-N recommendation. Social network regularized Sparse Linear Model (SocSLIM) with its extensions incorporating local learning (LocSocSLIM) to improve efficiency are proposed. SocSLIM learns sparse coefficient matrix for users by solving a sparse representation problem over user-item rating/purchase matrix and user-user social network's adjacency matrix at the same time by sharing coefficient matrix. The coefficient matrix is used to predict the recommendation scores, which are then combined with a proposed item based Distance regularized Sparse Linear Model (DSLIM) to generate recommendations for the users. The experimental results demonstrate that SocSLIM effectively uses the social information to outperform the state-of-the-art methods by at least 12%. Moreover, the local weight learning extension LocSocSLIM significantly improves the efficiency up to 10 times as compared to SocSLIM as the original SLIM while achieving the close performance guarantees. (C) 2016 Elsevier Ltd. All rights reserved.