The Sound of Silence: Mining Implicit Feedbacks to Compute Reputation
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A reliable mechanism for scoring the reputation of sellers is crucial for the development of a successful environment for customer-to-customer e-commerce. Unfortunately, most C2C environments utilize simple feedback-based reputation systems, that not only do not offer sufficient protection from fraud, but tend to overestimate the reputation of sellers by introducing a strong bias toward maximizing the volume of sales at the expense of the quality of service.
In this paper we present a method that avoids the unfavorable phenomenon of overestimating the reputation of sellers by using implicit feedbacks. We introduce the notion of an implicit feedback and we propose two strategies for discovering implicit feedbacks. We perform a twofold evaluation of our proposal. To demonstrate the existence of the implicit feedback and to propose an advanced method of implicit feedback discovery we conduct experiments on a large volume of real-world data acquired from an online auction site. Next, a game-theoretic approach is presented that uses simulation to show that the use of the implicit feedback can improve a simple reputation system such as used by eBay. Both the results of the simulation and the results of experiments prove the validity and importance of using implicit feedbacks in reputation scoring.
KeywordsLorenz Curve Reputation System Online Auction Good Agent Reputation Score
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