Biblio

Trust relationship prediction among people provides valuable supports for decision making, information dissemination, and product promotion in online social networks. Network embedding has achieved promising performance for link prediction by learning node representations that encode intrinsic network structures. However, most of the existing network embedding solutions cannot effectively capture the properties of a trust network that has directed edges and nodes with in/out links. Furthermore, there usually exist rich user attributes in trust networks, such as ratings, reviews, and the rated/reviewed items, which may exert significant impacts on the formation of trust relationships. It is still lacking a network embedding-based method that can adequately integrate these properties for trust prediction. In this work, we develop an AtNE-Trust model to address these issues. We firstly capture user embedding from both the trust network structures and user attributes. Then we design a deep multi-view representation learning module to further mine and fuse the obtained user embedding. Finally, a trust evaluation module is developed to predict the trust relationships between users. Representation learning and trust evaluation are optimized together to capture high-quality user embedding and make accurate predictions simultaneously. A set of experiments against the real-world datasets demonstrates the effectiveness of the proposed approach.

Trust prediction in online social networks is crucial for information dissemination, product promotion, and decision making. Existing work on trust prediction mainly utilizes the network structure or the low-rank approximation of a trust network. These approaches can suffer from the problem of data sparsity and prediction accuracy. Inspired by the homophily theory, which shows a pervasive feature of social and economic networks that trust relations tend to be developed among similar people, we propose a novel deep user model for trust prediction based on user similarity measurement. It is a comprehensive data sparsity insensitive model that combines a user review behavior and the item characteristics that this user is interested in. With this user model, we firstly generate a user's latent features mined from user review behavior and the item properties that the user cares. Then we develop a pair-wise deep neural network to further learn and represent these user features. Finally, we measure the trust relations between a pair of people by calculating the user feature vector cosine similarity. Extensive experiments are conducted on two real-world datasets, which demonstrate the superior performance of the proposed approach over the representative baseline works.

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.