An auctioning reputation system based on anomaly

ABSTRACT

Existing reputation systems used by online auction houses do not address the concern of a buyer shopping for commodities - finding a good bargain. These systems do not provide information on the practices adopted by sellers to ensure profitable auctions. These practices may be legitimate, like imposing a minimum starting bid on an auction, or fraudulent, like using colluding bidders to inflate the final price in a practice known as shilling.We develop a reputation system to help buyers identify sellers whose auctions seem price-inflated. Our reputation system is based upon models that characterize sellers according to statistical metrics related to price inflation. We combine the statistical models with anomaly detection techniques to identify the set of suspicious sellers. The output of our reputation system is a set of values for each seller representing the confidence with which the system can say that the auctions of the seller are price-inflated.We evaluate our reputation system on 604 high-volume sellers who posted 37,525 auctions on eBay. Our system automatically pinpoints sellers whose auctions contain potential shill bidders. When we manually analyze these sellers' auctions, we find that many winning bids are at about the items' market values, thus undercutting a buyer's ability to find a bargain and demonstrating the effectiveness of our reputation system.

REFERENCES

• 1: P. Bajari and A. Hortaçsu. The winner's curse, reserve prices, and endogenous entry: Empirical insights from eBay auctions. RAND Journal of Economics, 34(2):329--355, Summer 2003.
• 2: R. J. Bolton and D. J. Hand. Statistical fraud detection: A review. Statistical Science, 17(3):235--255, 2002.
• 3: R. Brause , T. Langsdorf , M. Hepp, Neural Data Mining for Credit Card Fraud Detection, Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, p.103, November 08-10, 1999
• 4: U. Brinkmann and M. Seifert. `Face to Interface': Zum Problem der Vertrauenskonstitution im Internet am Beispiel von elektronischen Auktionen. Zeitschrift für Soziologie, 30(1):23--47, Feb. 2001.
• 5: P. Chaudhuri and W.-Y. Loh. Nonparametric estimation of conditional quantiles using quantile regression trees. Bernoulli, 8:561--576, 2002.
• 6: Mao Chen , Jaswinder Pal Singh, Computing and using reputations for internet ratings, Proceedings of the 3rd ACM conference on Electronic Commerce, p.154-162, October 14-17, 2001, Tampa, Florida, USA [doi>10.1145/501158.501175]
• 7: Chrysanthos Dellarocas, Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior, Proceedings of the 2nd ACM conference on Electronic commerce, p.150-157, October 17-20, 2000, Minneapolis, Minnesota, United States [doi>10.1145/352871.352889]
• 8: Chrysanthos Dellarocas, The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms, Management Science, v.49 n.10, p.1407-1424, October 2003 [doi>10.1287/mnsc.49.10.1407.17308]
• 9: S. Dewan and V. Hsu. Trust in electronic markets: Price discovery in generalist versus specialty online auctions. Working paper, available a http://databases.si.umich.edu/reputations/bib/papers/Dewan&Hsu.doc, University of Michigan, Ann Arbor, MI.
• 10: Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
• 11: eBay Inc. Policy: Seller shill bidding. Published online at http://pages.ebay.com/help/policies/seller-shill-bidding. html (last accessed May 4, 2005).
• 12: eBay Inc. First quarter 2005 financial results, April 2005.
• 13: Z. Hidvégi, W. Wang, and A. B. Whinston. Shill-proof fee (SPF) schedule: the sunscreen against seller self-collusion in online English auctions. Working paper, Emory University, Atlanta, GA.
• 14: D. Houser and J. Wooders. Reputation in auctions: Theory, and evidence from eBay. Journal of Economics and Management Strategy, 2004.
• 15: J. H. Kagel and D. Levin. Common Value Auctions and the Winner's Curse. Princeton University Press, 2002.
• 16: Robert J. Kauffman , Charles A. Wood, Running up the bid: detecting, predicting, and preventing reserve price shilling in online auctions, Proceedings of the 5th international conference on Electronic commerce, p.259-265, September 30-October 03, 2003, Pittsburgh, Pennsylvania [doi>10.1145/948005.948040]
• 17: R. Koenker and G. Bassett. Regression quantiles. Econometrica, 46:33--50, 1978.
• 18: Christopher Krügel , Thomas Toth , Engin Kirda, Service specific anomaly detection for network intrusion detection, Proceedings of the 2002 ACM symposium on Applied computing, March 11-14, 2002, Madrid, Spain [doi>10.1145/508791.508835]
• 19: Wenke Lee , Salvatore J. Stolfo, A data mining framework for constructing features and models for intrusion detection systems (computer security, network security), 1999
• 20: W. Lee, S. J. Stolfo, and K. W. Mok. A data mining framework for building intrusion detection models. In IEEE Symposium on Security and Privacy, pages 120--132, Oakland, CA, 1999.
• 21: Yves Moreau , Herman Verrelst , Joos Vandewalle, Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype, Proceedings of the 7th International Conference on Artificial Neural Networks, p.1065-1070, October 08-10, 1997
• 22: New York Attorney General. Shill bidding exposed in online auctions. Press release, Nov. 2004.
• 23: Paul Resnick , Ko Kuwabara , Richard Zeckhauser , Eric Friedman, Reputation systems, Communications of the ACM, v.43 n.12, p.45-48, Dec. 2000 [doi>10.1145/355112.355122]
• 24: P. Resnick and R. Zeckhauser. Trust among strangers in Internet transactions: Empirical analysis of eBay's reputation system. In M. R. Baye, editor, The Economics of the Internet and E-Commerce, volume 11 of Advances in Applied Microeconomics. Elsevier Science, Amsterdam, Netherlands, 2002.
• 25: P. Resnick, R. Zeckhauser, J. Swanson, and K. Lockwood. The value of reputation on eBay: A controlled experiment. Working paper, available at http://www.si.umich.edu/~presnick/papers/postcards/, University of Michigan, Ann Arbor, MI.
• 26: J. A. Rice. Mathematical Statistics and Data Analysis. Duxbury Press, 2nd edition, 1994.
• 27: H. S. Shah, N. R. Joshi, A. Sureka, and P. R. Wurman. Mining eBay: Bidding strategies and shill detection. In Springer-Verlag, editor, 4th International Workshop on Mining Web Data for Discovering Usage Patterns and Profiles, Edmonton, Alberta, July 2003.

INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.4 COMPUTERS AND SOCIETY
      K.4.4 Electronic Commerce

General Terms:
Economics, Experimentation, Security

Keywords:
anomaly detection, auction, reputation system, shilling