Modeling Trust in Open, Dynamic Multi-agent Systems and Developing Framework for Predicting Consumer-generated Reviews' Helpfulness

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The goal of this proposed research program is twofold: (a) to construct an effective trust model for open, dynamic multi-agent systems, and (b) to develop a framework for modeling the helpfulness of online reviews. Modeling trust is of vital importance to open multi-agent systems where agents need to find trustworthy partners to help fulfill their tasks while deceptive agents may exist in the environment. We plan to build a trust model that works effectively not only when agents can gather sufficient evidence to evaluate the trustworthiness of others, but also when trust evidence is unavailable (e.g., in highly dynamic systems with new agents continually joining and leaving). Our approach is to compute an agent's trust as an accurate function of direct and indirect evidence, and to make use of incentive mechanisms and machine learning to promote honesty and learn the similarities and dissimilarities between new and existing agents. We expect to achieve a trust model that is valuable to many important application domains such as electronic marketplaces, social networks, vehicular ad-hoc networks, cloud computing environments, and peer-to-peer systems, etc. Online product reviews have become a major source of information to help consumers make good purchase decisions. However, due to the vast number of available reviews and their great difference in the level of helpfulness, consumers really need systems that can discover and recommend the most helpful reviews to them. We would like to develop a rigorous framework for inferring the helpfulness of reviews, which uses a probabilistic approach to formulate helpfulness inference as an optimization problem. We will use our preliminary model for helpfulness prediction as a starting point, but will extend it considerably to obtain a more complex model with significantly increased performance and improved accuracy. This framework is expected to be useful for several applications: Search engines, e-commerce websites, online communities and recommender systems can utilize this framework to offer the most helpful reviews, suitable products, services and/or vendors to their users.