A unified framework for link and rating prediction in multi-modal social networks

Abstract

Multi-modal social networks (MSNs) allow users to form explicit (by adding new friends in their network) or implicit (by similarly co-rating items) social networks. Previous research work was limited either to the prediction of new relationships among users (i.e., link prediction problem) or to the prediction of item ratings (i.e., rating prediction problem and item recommendations). In this paper, we develop a framework to incorporate both research directions into a unified model. Our social-union algorithm combines similarity matrices derived from heterogeneous (unipartite and bipartite) explicit or implicit MSNs. We perform an extensive experimental comparison of the proposed method against existing link and rating prediction algorithms, using synthetic and two real data sets (Epinions and Flixter). Our experimental results show that our social-union framework is more effective in both rating and link prediction.