DeepTrust: A Deep User Model of Homophily Effect for Trust Prediction 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 lowrank 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 useru0027s latent features mined from user review behavior and the item properties that the user cares. Then we develop a pairwise 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 realworld datasets, which demonstrate the superior performance of the proposed approach over the representative baseline works.

Social Trust Network Embedding Developing effective network embedding methods for social trust networks (STNs) is a nontrivial problem because two key pieces of information need to be preserved simultaneously: a useru0027s relations to latent factors and the trust transfer patterns that govern what type of relationship will form. In this study, we propose a novel social trust network embedding method (STNE) to address these issues. Specifically, we present a modified SkipGram model with negative sampling to jointly learn latent factor features, along with the trust transfer pattern features. Moreover, we define a flexible notion about a useru0027s latent relationships with other users, which generates reliable negative samples for optimization. Extensive experiments on several realworld networks demonstrate the efficacy of the proposed STNE.