Performance measurement and data base design
Alfonso P. Cardenas, Larry F. Bowman, et al.
ACM Annual Conference 1975
Exponential growth of information generated by online social networks demands effective and scalable recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social contextual information has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users' motivation of social behaviors into social recommendation. In this paper, we investigate the social recommendation problem 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 behavior prediction. Then we propose a novel probabilistic matrix factorization method to fuse them in latent space. We further provide a scalable algorithm which can incrementally process the large scale data. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network data sets. The empirical results and analysis on these two large data sets demonstrate that our method significantly outperforms the existing approaches.
Alfonso P. Cardenas, Larry F. Bowman, et al.
ACM Annual Conference 1975
György E. Révész
Theoretical Computer Science
Pradip Bose
VTS 1998
Elliot Linzer, M. Vetterli
Computing