Making recommendations from top-N user-item subgroups

Group-aware collaborative filtering (CF) has recently become a hot research topic in recommender systems, which typically divides a large CF task on the entire data (i.e. rating matrix) into some smaller CF tasks on subgroups (i.e., sub-matrices). This leads to an effective way to improve current CF systems in accuracy and efficiency. However, existing approaches consider each subgroup separately, ignoring relationships among subgroups. In this paper, motivated by the intuition that there are similar users or items among different subgroups, we propose an improved group-aware CF algorithm which predicts a rating using a weighted sum of similar ratings from multiple subgroups. Our algorithm is based on Matrix Factorization and CodeBook Transfer (CBT), especially that we construct N matrix approximations based on N best sub-matrices, and then integrate the N approximations via a linear combination. We conduct experiments on real-life data to evaluate the performance of our algorithm in comparison with traditional CF algorithms and other state-of-the-art social and group-aware recommendation models. The empirical result and analysis demonstrate that our algorithm achieves a significant increase in recommendation accuracy.