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 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2005
- Accession Number
- ADA449098
Entities
People
- Hao Wang
- Jonathon T. Giffin
- Louis Kruger
- Mihai Christodorescu
- Shai Rubin
- Vinod Ganapathy
Organizations
- University of Wisconsin Madison Department of Computer Science