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dc.creatorZhang,Jie
dc.date2011-12-01
dc.date.accessioned2019-04-25T12:41:36Z
dc.date.available2019-04-25T12:41:36Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0718-18762011000300005
dc.identifier.urihttp://revistaschilenas.uchile.cl/handle/2250/61137
dc.descriptionThe 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.
dc.formattext/html
dc.languageen
dc.publisherUniversidad de Talca
dc.relation10.4067/S0718-18762011000300005
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceJournal of theoretical and applied electronic commerce research v.6 n.3 2011
dc.subjectTrust and reputation systems
dc.subjectUnfair ratings
dc.subjectElectronic marketplaces
dc.subjectProbabilistic reasoning approaches
dc.subjectMulti-agent systems
dc.titleExtensive Experimental Validation of a Personalized Approach for Coping with Unfair Ratings in Reputation Systems


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