Abstract
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.
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References
Axelrod, R.M.: The Evolution of Cooperation. Basic Books (1984)
Ba, S., Whinston, A.B., Zhang, H.: Building trust in online auction markets through an economic incentive mechanism. Decision Support Systems 35, 273–286 (2003)
Gmytrasiewicz, P., Durfee, E.: Toward a theory of honesty and trust among communicating autonomous agents. Group Decision and Negotiation 2, 237–258 (1993)
Kostreva, M.M., Ogryczak, W., Wierzbicki, A.: Equitable aggregations and multiple criteria analysis. European Journal of Operational Research 158(2), 362–377 (2004)
Malaga, R.A.: Web-based reputation management systems: Problems and suggested solutions. Electronic Commerce Research 4(1) (2001)
Marshall, A.W., Olkin, I.: Inequalities: Theory of Majorization and Its Applications. Academic Press, London (1979)
Melnik, M.I., Alm, J.: Does a seller’s ecommerce reputation matter? evidence from ebay auctions. The Journal of Industrial Economics L(3) (September 2002)
Morzy, M.: New Algorithms for Mining the Reputation of Participants of Online Auctions. In: Deng, X., Ye, Y. (eds.) WINE 2005. LNCS, vol. 3828, pp. 112–121. Springer, Heidelberg (2005)
Mui, L.: Computational Models of Trust and Reputation: Agents, Evolutionary Games, and Social Networks. PhD thesis, Massachusetts Institute of Technology (2003)
Resnick, P., Zeckhauser, R.: Trust among strangers in internet transactions: Empirical analysis of ebay’s reputation system. Advances in Applied Microeconomics 11 (2002)
Resnick, P., Zeckhauser, R., Friedman, E., Kuwabara, K.: Reputation systems. Communications of the ACM 43(12) (2000)
Resnick, P., Zeckhauser, R., Swanson, J., Lockwood, K.: The value of reputation on ebay: a controlled experiment. Technical report, School of Information, University of Michigan (2004)
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Morzy, M., Wierzbicki, A. (2006). The Sound of Silence: Mining Implicit Feedbacks to Compute Reputation. In: Spirakis, P., Mavronicolas, M., Kontogiannis, S. (eds) Internet and Network Economics. WINE 2006. Lecture Notes in Computer Science, vol 4286. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11944874_33
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DOI: https://doi.org/10.1007/11944874_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68138-0
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