Electrostatic Force Method:
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Online auctions are among the most important e-commerce services. Unfortunately it is very difficult to assure trust in such customer-to-customer environment. Most auction sites utilize a very simple participation counts system for reputation rating. This feedback-based reputation systems do not differentiate between sellers who trade in luxury goods and those who sell worthless trinkets. A fraudster can easily gain reputation by selling hundreds of cheap books and then cheat while selling a few expensive TV sets which are not as good as described on item page.
In this paper we present a novel trust management method called Electrostatic Force Method (EFM) which calculates Personal Subjective Trust instead of overall reputation value. The trust value depends on price and category of an item one wants to buy. In this method a seller could have high trust value for someone who wants to buy a book and at the same time this seller may not be trustworthy for someone who wants to buy a TV set. Furthermore our method can be applied in addition to the system currently used by eBay-like online auction sites because it does not require any additional information other than positive, negative or neutral feedback on transactions.
Keywordsonline auction sites reputation system trust management method
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- 1.DeFigueiredo, D., Barr, E.T., Wu, S.F.: Trust Is in the Eye of the Beholder. In: CSE, vol. (3), pp. 100–108 (2009)Google Scholar
- 3.Kwan, M.Y.K., Overill, R.E., Chow, K.P., Silomon, J.A.M., Tse, H., Law, F.Y.W., Lai, P.K.Y.: Evaluation of Evidence in Internet Auction Fraud Investigations. In: IFIP Int. Conf. Digital Forensics, pp. 121–132 (2010)Google Scholar
- 4.Leszczyński, K.: Asymptotic Trust Algorithm: Extension for reputation systems in online auctions. In: KKNTPD 2010 - III Krajowa Konferencja Naukowa Technologie Przetwarzania Danych (2010)Google Scholar
- 6.Marsh, S.P.: Formalising Trust as a Computational Concept. Ph.D. thesis, Department of Mathematics and Computer Science, University of Stirling (1994)Google Scholar
- 9.O’Donovan, J., Evrim, V., Smyth, B., McLeod, D., Nixon, P.: Personalizing Trust in Online Auctions. In: STAIRS, pp. 72–83 (2006)Google Scholar
- 10.Resnick, P., Zeckhauser, R.: Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay’s Reputation System. The Economics of the Internet and E-Commerce 11(2), 23–25 (2002)Google Scholar
- 11.Simpson, E.H.: The Interpretation of Interaction in Contingency Tables. Journal of the Royal Statistical Society, Ser. B 13 (1951)Google Scholar
- 12.Xiong, L., Liu, L.: A Reputation-Based Trust Model for Peer-to-Peer eCommerce Communities. In: IEEE International Conference on E-Commerce Technology, p. 275 (2003)Google Scholar
- 14.Zhang, H., Duan, H.X., Liu, W.: RRM: An incentive reputation model for promoting good behaviors in distributed systems. Science in China Series F: Information Sciences 51(11), 1871–1882 (2008)Google Scholar
- 15.Alexa Top 500 Global Web Sites, top 500 sites by alexa traffic ranking, http://www.alexa.com/topsites/global/
- 16.Allegro, the leading Polish provider of online auctions, http://allegro.pl/
- 17.ebay, the worldwide online auctions, http://www.ebay.com/
- 18.Ranking of sites. The 1000 most-visited sites on the web, http://www.google.com/adplanner/static/top1000/
- 19.Taobao, chinese auction portal with over 100 million users, http://taobao.com