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

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). Recent link prediction methods infer future relationships among users, by also exploiting information from their implicit networks (i.e. user-item rating network). On the other hand, Rating Prediction methods predict the user’s rating behavior on items, by also exploiting information from their explicit network (i.e. friendship network). In this paper, we develop a framework to incorporate both research directions into a unified model. We extend our Social-Union algorithm, which initially focused on the rating prediction problem, in order to be applied also on the link prediction problem. Social-Union combines similarity matrices derived from heterogeneous (unipartite and bipartite) explicit or implicit MSNs. Moreover, we propose an effective weighting strategy of MSNs influence based on their structured density. We also generalize our model for combining multiple social networks. 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. 2 Panagiotis Symeonids, Eleftherios Tiakas and Yannis Manolopoulos