Abstract
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.
Highlights
In electronic marketplaces populated by self-interested agents, buying agents would benefit by modeling the trustworthiness of selling agents, in order to make effective decisions about which agents to trust
We can see that the difference in false positive rate (FPR), false negative rate (FNR) and Matthew's correlation coefficient (MCC) is larger when buyers do not have much experience with sellers
We presented a personalized approach for effectively handling unfair ratings in centralized reputation systems
Summary
In electronic marketplaces populated by self-interested agents, buying agents would benefit by modeling the trustworthiness of selling agents, in order to make effective decisions about which agents to trust. To cope with the problem of unfair ratings, researchers have been developing proactive approaches that create incentives for buyers to provide fair ratings [4], [12], [41]. These approaches have to be deployed in the marketplaces since the very beginning. We present a personalized modeling approach (PMA) for coping with unfair ratings in reputation systems This approach allows a buyer to model the trustworthiness of advisors by combining the buyer’s personal experience with the advisors and the public knowledge about them held by the system.
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