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Journal of theoretical and applied electronic commerce research

versión On-line ISSN 0718-1876


ZHANG, Jie. Extensive Experimental Validation of a Personalized Approach for Coping with Unfair Ratings in Reputation Systems. J. theor. appl. electron. commer. res. [online]. 2011, vol.6, n.3, pp.43-64. ISSN 0718-1876.

The unfair rating problem exists when a buying agent models the trustworthiness of selling agents by also relying on ratings of the sellers from other buyers in electronic marketplaces, that is in a reputation system. In this article, we first analyze the capabilities of existing approaches for coping with unfair ratings in different challenging scenarios, including ones where the majority of buyers are dishonest, buyers lack personal experience with sellers, sellers may vary their behavior, and buyers may provide a large number of ratings. We then present a personalized modeling approach (PMA) that has all these capabilities. Our approach allows a buyer to model both the private reputation and public reputation of other buyers to determine whether these buyers' ratings are fair. More importantly, in this work, we focus on experimental comparison of our approach with two key models in a simulated dynamic e-marketplace environment. We specifically examine the above mentioned scenarios to confirm our analysis and to demonstrate the capabilities of our approach. Our study thus provides the extensive experimental support for the personalized approach that can be effectively employed by reputation systems to cope with unfair ratings.

Palabras clave : Trust and reputation systems; Unfair ratings; Electronic marketplaces; Probabilistic reasoning approaches; Multi-agent systems.

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