Most of traditional recommender systems perform well only when sufficient user-item interactions, such as purchase records or ratings, have been obtained in advance, while suffering from poor performance in the scenario of sparse interactions. Addressing this problem, we propose a neural network based recommendation framework which is fed with user/item's original tags as well as the expanded tags from social context. Through embedding the latent correlations between tags into distributed feature representations, our model uncovers the implicit relationships between users and items sufficiently, exhibiting superior performance no matter whether sufficient user-item interactions are available or not. Furthermore, our framework can be further tailored for link prediction in networks, since recommending an item to a user can be recognized as predicting a link between them. The extensive experiments on two real recommendation tasks, i.e., Weibo followship recommendation and Douban movie recommendation, justify our framework's superiority to the state-of-the-art methods.
