A statistical relational model for trust learning

We address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn contextsensitive trust using statistical relational learning in form of the Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering ...

• Type: Conference
• Year: 2008
• Where: ATAL
• Authors: Achim Rettinger, Matthias Nickles, Volker Tresp
• Keywords: ATAL 2008, Intelligent Agents, Relational Learning, Trust, Trust Assessment