Learning to recommend with trust and distrust relationships

With the exponential growth of Web contents, Recommender System has become indispensable for discovering new information that might interest Web users. Despite their success in the industry, traditional recommender systems suffer from several problems. First, the sparseness of the user-item matrix seriously affects the recommendation quality. Second, traditional recommender systems ignore the connections among users, which loses the opportunity to provide more accurate and personalized recommendations. In this paper, aiming at providing more realistic and accurate recommendations, we propose a factor analysis-based optimization framework to incorporate the user trust and distrust relationships into the recommender systems. The contributions of this paper are three-fold: (1) We elaborate how user distrust information can benefit the recommender systems. (2) In terms of the trust relations, distinct from previous trust-aware recommender systems which are based on some heuristics, we systematically interpret how to constrain the objective function with trust regularization. (3) The experimental results show that the distrust relations among users are as important as the trust relations. The complexity analysis shows our method scales linearly with the number of observations, while the empirical analysis on a large Epinions dataset proves that our approaches perform better than the state-of-the-art approaches.