An Auctioning Reputation System Based on Anomaly Detection

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 met-rics 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 win-ning 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.

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