MemTrust: Find Deep Trust in Your Mind

Abstract

Trust prediction is gaining significant interest since it could reduce the burden of user decision-makings effectively in various social activities. Existing works on trust prediction mainly based on trust networks, however, usually give little consideration to data sparsity and temporal continuity of user behavior. In order to solve these problems, we propose a comprehensive deep MemTrust model for trust prediction. With this model, we introduce a embedding layer to extend the feature space and alleviate the distinctive information oblivion caused by data sparsity. In addition, Long Short-Term Memory(LSTM) network is utilized to extract overall time series features through the multiple time slices of user features. Finally, the trust is estimated by pairwise time series features of users. Extensive experiments are validated on two real datasets, which demonstrate that the proposed model has superior performance compared with representative baseline approaches.