Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks

Abstract

Trust prediction facilitates the day-to-day functionality of diverse web-based applications, such as recommendation systems, market advertising and anomaly detection. However, existing works heavily rely on user–user trust interactions, which result in limited performance as the data sparsity. Previous studies have shown that the trust relationship between users is significantly affected by the category of items that the users interacted. In this paper, we propose a MetaTrust model, which generates redundant user-item interactions as the supplement of user–user trust to alleviate the data sparsity on trust prediction. Specifically, we propose category-aware metapaths, which generate abundant user–item–user interactions based on the common item category that users have interacted with. Further, Long Short Term Memory (LSTM) networks are utilized to mine features of multiple category-aware metapaths and their correlations. In order to filter the user–item–user interactions that are not related to the current task, the real trust relationship between users are embedd in the network with MLP. Finally, a multi-headed attention network is utilized to distinguish which metapath determines trust prediction between the current pair of users. Extensive experiments on three real-world dataset show that our proposed model can effectively achieve significant improvements over other competitive approaches and show the potential interpretability of trust building.