Fuzzy computational models for trust and reputation systems

Abstract

In the recent past, a considerable research has been devoted to trust and reputation mechanisms to simplify complex transactions for open environments in social networking, e-commerce, and recommender systems (RS). In real life, we come to know about others through our social circle according to their reputation which is a public view. However, it is not always adequate to depend solely on the public view and therefore a trust measure is required to give a personalized view of the future encounters with a specific partner. In this paper, we propose fuzzy computational models for both trust and reputation concepts. Reciprocity and experience are used for trust modeling while the proposed reputation model is a fuzzy extension of beta reputation model. A two-level filtering methodology is proposed to benefit to a large extent from both the concepts separately. In order to justify the proposed models, we compared them with the existing reputation models for movie RS. The experimental results show that the incorporation of trust and reputation concepts into RS indeed improves the recommendation accuracy and establish that our models are better than beta and the popular eBay reputation models.