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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. Different probabilistic approaches have been proposed to cope with this issue. In this paper, we first summarize these approaches and provide a detailed categorization of them. This includes our own "personalized" approach for addressing this problem. Based on the implication of such analysis, we then focus on experimental comparison of our approach with two key models in a framework that simulates a dynamic electronic marketplace environment. We specifically examine different 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. Our study provides the basis for deciding which approach is most appropriate to employ, in which scenario.