Exploiting Implicit and Explicit Signed Trust Relationships for Effective Recommendations

Trust networks have been widely used to mitigate the data sparsity and cold-start problems of collaborative filtering. Recently, some approaches have been proposed which exploit explicit signed trust relationships, i.e., trust and distrust relationships. These approaches ignore the fact that users despite trusting/distrusting each other in a trust network may have different preferences in real-life. Most of these approaches also handle the notion of the transitivity of distrust as well as trust. However, other existing work observed that trust is transitive while distrust is intransitive. Moreover, explicit signed trust relationships are fairly sparse and may not contribute to infer true preferences of users. In this paper, we propose to create implicit signed trust relationships and exploit them along with explicit signed trust relationship to solve sparsity problem of trust relationships. We also confirm the similarity (resp. dissimilarity) of implicit and explicit trust (resp. distrust) relationships by using the similarity score between users so that users' true preferences can be inferred. In addition to these strategies, we also propose a matrix factorization model that simultaneously exploits implicit and explicit signed trust relationships along with rating information and also handles transitivity of trust and intransitivity of distrust. Extensive experiments on Epinions dataset show that the proposed approach outperforms existing approaches in terms of accuracy.