SBMF: Similarity-Based Matrix Factorization for Collaborative Recommendation

Matrix factorization (MF) has been proved a very successful technique for Collaborative Filtering (CF), and hence has been widly adpoted in today´s recommender systems. However, many studies have been proved that MF alone is poor to reveal the local relationships of users and items which can be learned well by the neighborhood-aware methods. To combine the merits of both approaches, in this paper, we propose a novel model which can effectively integrate the local preference information into MF. Different from various proposed methods which focus on representing the local similarity by the interactions of corresponding latent factors, we extend the neighborhood relationships to both latent factors and their rating preference. First, we establish clusters of users and items based on neighborhood information. Second, we transform the cluster information into two rating matrices which represent (user cluster) - (item) and (user) - (item cluster) preference. Third, we combine the generated rating matrices and the local latent factors into a single model, named Similarity-Based Matrix Factorization (SBMF). Since our model can explore the external representation of similarity information, it leads to more accurate recommendations. Experimental results on several real-world data sets show that our SBMF outperforms the state-of-the-art methods.