Advanced Feedback Management for Internet Auction Reputation Systems

Abstract

Electronic auction reputation systems have improved in recent years. However, most don't rely on user feedback but are still bound to old-fashioned comment counting while substantial information embedded in those comments is omitted. The authors' system manages and learns from user feedback and considers auctions' context, possible types of complaints, and the structure of connections between those complaints. They propose amplifying a reputation system algorithm to estimate the reported complaints' harmfulness. Their results are based on a real-world dataset from a leading Eastern European online auction provider.