Advanced Feedback Management for Internet Auction Reputation Systems

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.