Deep Modeling of Social Relations for Recommendation
Social based recommender systems have been recently proposed by incorporating social relations of users to alleviate sparsity issue of user to item rating data and to improve recommendation performance. Many of these social based recommender systems linearly combine the multiplication of social features between users. However, these methods lack the ability to capture complex and intrinsic non linear features from social relations. In this paper, we present a deep neural network based model to learn non linear features of each user from social relations, and to integrate into probabilistic matrix factorization for rating prediction problem. Experiments demonstrate the advantages of the proposed method over stateof the art social based recommender systems.
AAAI