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.

Introduction

The emerging online environment let people buy and sell goods, play, and recommend products for each other without knowing the remote partner. All of these encounters are only possible if partners trust each other. Consequently they can delegate tasks and decisions to an appropriate person and therefore improving the quality of online markets [1], [2]. However, insufficient trust in e-commerce could lead to users ‘staying away’ from the technology altogether. Despite obvious importance of trust and reputation, transferring them to computer science is still a challenging problem. Computer scientists from many areas, e.g., security, semantic web, and e-commerce, are still working on the transfer of these concepts to be used in computer mediated transactions. It is often hard to assess the trustworthiness of remote partners in the computer mediated transactions and processes because familiar styles of interaction are still far from being computationally modeled easily. Actually, physical transactions and traditional forms of communication like body language, gestures, and facial expressions allow people to assess a much wider range of cues related to trustworthiness than the current computer mediated communication can do.

Amongst different areas of computer science, e-commerce seems to be the most benefitted area from the trust and reputation concepts. Recently, very big electronic communities have been established and grown very fast. Unfortunately, a major weakness of e-markets is the raised level of risk associated with the loss of the notions of trust and reputation. The consumer is usually forced to accept the ‘risk of prior performance’, i.e. to pay for services and goods before receiving them, which can leave him in a vulnerable position. On the other hand, the service provider has much more information about the product quality than the customer, as long as he will receive payment before shipment in most cases. The research work in the recent past [1], [3], [4], [5], [6], [7], [8] shows that this information asymmetry can be mitigated through trust and reputation concepts. The idea is that even if the consumer cannot try the service in advance, he can be confident that it will be what he expects as long as he trusts the service provider.

Various definitions have been given in the literature to both trust and reputation concepts. For example, Ruohomaa and Kutvonen [7] defined trust as the extent to which one partner is willing to participate in a given action with another partner, considering the risks and incentives involved. On the other hand, the reputation is defined by Mui et al. [8] as a perception a partner creates through past actions about his intentions and norms. Although the way of incorporating trust and reputation concepts in e-commerce varies from system to system but they provide an incentive for good behavior and a punishment for bad behavior. However, trust and reputation concepts are social fuzzy concepts thus fuzzy computational models for both the concepts would reflect their actual value for enhancing the system accuracy.

The rest of this paper is organized as follow: trust and reputation concepts in literature are given in Section 2. The proposed fuzzy computational models for trust and reputation are presented in Section 3 and are employed for movie RS in Section 4. Computational experiments and results are given in Section 5. Finally, the last section concludes the paper and gives some future research directions.