CGMF: Coupled Group-Based Matrix Factorization for Recommender System

With the advent of social influence, social recommender systems have become an active research topic for making recommendations based on the ratings of the users that have close social relations with the given user. The underlying assumption is that a user’s taste is similar to his/her friends’ in social networking. In fact, users enjoy different groups of items with different preferences. A user may be treated as trustful by his/her friends more on some specific rather than all groups. Unfortunately, most of the extant social recommender systems are not able to differentiate user’s social influence in different groups, resulting in the unsatisfactory recommendation results. Moreover, most extant systems mainly rely on social relations, but overlook the influence of relations between items. In this paper, we propose an innovative coupled group-based matrix factorization model for recommender system by leveraging the user and item groups learned by topic modeling and incorporating couplings between users and items and within users and items. Experiments conducted on publicly available data sets demonstrate the effectiveness of our approach.