Association for Computing Machinery
Trust inference, which is the mechanism to build new pair-wise trustworthiness relationship based on the existing ones, is a fundamental integral part in many real applications, e.g., e-commerce, social networks, peer-to-peer networks, etc. State-of-the-art trust inference approaches mainly employ the transitivity property of trust by propagating trust along connected users (a.k.a. trust propagation), but largely ignore other important properties, e.g., prior knowledge, multi-aspect, etc. In this paper, the authors propose a multi-aspect trust inference model by exploring an equally important property of trust, i.e., the multi-aspect property. The heart of their method is to view the problem as a recommendation problem, and hence opens the door to the rich methodologies in the field of collaborative filtering.