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.