Abstract :
Traditional Collaborative Filtering has been one of the most widely used recommender systems, unfortunately it suffers from cold-start and data sparsity problems. With the development of social networks, more recommendation systems are trying to generate more eligible recommendation through excavating users´ potential preferences using their social relationships. Almost all social recommender systems employ only positive inter-user relations such as friendship or trust information. However, incorporating negative relations in recommendation has not been investigated thoroughly in literature. In this paper, we propose a novel model-based method which takes advantage of both positive and negative inter-user relations. We apply matrix factorization techniques and utilize both rating and trust information to learn users´ reasonable latent preference. We also incorporate two regularization terms to take distrust information into consideration. Our experiments on real-world and open datasets demonstrate the superiority of our model over the other state-of-the-art methods.
