Density-based measure of reputation of sellers in online auctions
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ADBIS Workshop on Data Mining and Knowledge Discovery (ADMKD'2005) / sympozjum [1st; September 15-16, 2005; Tallinn, Estonia]
Online auctions are gaining tremendous popularity in recent years. Although providing unprecedent opportunities, online auction sites become an attractive environment for fraud. The expansion of the share of online auctions in the world trade causes exponential growth of theft and deception associated with this medium. Participants of online auctions agree that trustworthy reputation systems are an important factor in fighting dishonest and malicious users. Unfortunately, popular auction sites use only very simple reputation estimation schemes that utilize feedbacks issued reciprocally by users after terminated auctions. Such systems can be easily deceived and do not offer sufficient protection against organized fraud. In this paper we present a novel density-based reputation measure. The new reputation measure uses the topology of seller-buyer connections to derive knowledge about trustworthy sellers. We mine the data on past transactions to discover clusters of connected sellers and for each seller we measure the density of the seller's neighborhood. We use these clusters both for scoring the reputation of individual sellers, and to assist buyers in informed decision making by generating automatic recommendations. We perform many experiments on the body of real-world data acquired from a leading Polish provider of online auctions to examine the new measure in detail.
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