Social Contextual Recommendation

Exponential growth of information generated by online so-cial networks demands effective recommender systems to give useful results. Traditional techniques become unquali-fied because they ignore social relation data; existing social recommendation approaches consider social network struc-ture, but social context has not been fully considered. It is significant and challenging to fuse social contextual fac-tors which are derived from users ’ motivation of social be-haviors into social recommendation. In this paper, we in-vestigate social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online item adoption and recommendation. Then we pro-pose a novel probabilistic matrix factorization method to fuse them in latent spaces. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network datasets in China. The empirical result and analysis on these two large datasets demonstrate that our method significantly outperform the existing approaches