An adaptive group recommender based on overlapping community detection

In this paper, a kind of modified adaptive group recommender based on overlapping community detection (GROCD) is proposed. Different from existing recommenders, GROCD takes both of group members´ preferences and their complex internal interactions into account. In this research, both of overlapping community integration strategy and contribution-based collaborative filtering are employed to explore group members´ interests and provide the predicted group ratings on movies. The authors discuss the effectiveness of the proposed approach on Movielens dataset. The results show that the proposed recommender can achieve comparatively accurate prediction with a comparatively low computation complexity.