Integrating User Embedding and Collaborative Filtering for Social Recommendations

Social recommendation has attracted increasing attention over the years due to the potential value of social relations, which can be harnessed to mitigate the dilemma of data sparsity in traditional recommender systems. However, recent studies show that social recommenders fail in the practical use in industry for the reason that some problems in social relations, such as the noise, lead to a degradation in recommendation quality. To solve the problem, in this paper, a social recommender, SocialEM, which integrates the neural user embedding and collaborative filtering is proposed. Enlightened by the factorization of the word co-occurrence matrix which is equivalent to the skip-gram model in word2vec, SocialEM will jointly decomposes the user-item rating matrix and the user-user co-occurrence matrix with shared user latent factors. For each pair of users, the co-occurrence matrix encodes the number of being trusted together by other users in the social relation network. Experiments conducted on the real-world datasets have shown that the side effect of social relations can be diminished by tuning parameters for SocialEM. And compared with previous studies, our method significantly improves the quality of recommendations.