An auctioning reputation system based on anomaly

Existing reputation systems used by online auction houses do not address the concern of a buyer shopping for commodities—find ing 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 t o 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 i s based upon models that characterize sellers according to statist ical metrics related to price inflation. We combine the statistical m odels with anomaly detection techniques to identify the set of suspicious sellers. The output of our reputation system is a set of value s for each seller representing the confidence with which the syste m 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 b idders. When we manually analyze these sellers’ auctions, we find that man y winning bids are at about the items’ market values, thus undercu tting a buyer’s ability to find a bargain and demonstrating the effec tiveness of our reputation system.