Towards Context-aware Social Recommendation via Individual Trust

Incorporating social network information and contexts to improve recommendation performance has been drawing considerable attention recently. However, a majority of existing social recommendation approaches suffer from the following problems: (1) They only employ individual trust among users to optimize prediction solutions in user latent feature space or in user-item rating space; thus, they exhibit low recommendation accuracy. (2) They use decision trees to perform context-based user-item subgrouping; thus, they can only handle categorical contexts. (3) They have difficulty coping with the data sparsity problem. To solve these problems, and accurately and realistically model recommender systems, we propose a social matrix factorization method to optimize the prediction solution in both user latent feature space and user-item rating space using the individual trust among users. To further improve the recommendation performance and alleviate the data sparsity problem, we propose a context-aware enhanced model based on Gaussian mixture model (GMM). Two real datasets (one sparse dataset and one dense dataset) based experiments show that our proposed method outperforms the state-of-the-art social matrix factorization and context-aware recommendation methods in terms of prediction accuracy.