Biblio

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.

Given "who-trusts/distrusts-whom" information, how can we propagate the trust and distrust? With the appearance of fraudsters in social network sites, the importance of trust prediction has increased. Most such methods use only explicit and implicit trust information (e.g., if Smith likes several of Johnson's reviews, then Smith implicitly trusts Johnson), but they do not consider distrust. In this paper, we propose PIN-TRUST, a novel method to handle all three types of interaction information: explicit trust, implicit trust, and explicit distrust. The novelties of our method are the following: (a) it is carefully designed, to take into account positive, implicit, and negative information, (b) it is scalable (i.e., linear on the input size), (c) most importantly, it is effective and accurate. Our extensive experiments with a real dataset, Epinions.com data, of 100K nodes and 1M edges, confirm that PIN-TRUST is scalable and outperforms existing methods in terms of prediction accuracy, achieving up to 50.4 percentage relative improvement. 

Recently, PIN-TRUST, a method to predict future trust relationships between users is proposed. PIN-TRUST out-performs existing trust prediction methods by exploiting all types of interactions between users and the reciprocation of ones. In this paper, we validate whether its consideration on the reciprocation of interactions is really effective in trust prediction. Furthermore, we consider a new concept, the "uncertainty" of untrustworthy users that is devised to reflect the difficulty on modeling the activities of untrustworthy users in PIN-TRUST. Then, we also validate the effectiveness this uncertainty concepts. Through the validation, we reveal that the consideration of the reciprocation of interactions is effective for trust prediction with PIN-TRUST, and it is necessary to regard the uncertainty of untrustworthy users same as that of other users.