Communications of the ACM
Computing and using reputations for internet ratings
Proceedings of the 3rd ACM conference on Electronic Commerce
Managing trust in a peer-2-peer information system
Proceedings of the tenth international conference on Information and knowledge management
Web-Based Reputation Management Systems: Problems and Suggested Solutions
Electronic Commerce Research
A reputation-based trust model for peer-to-peer ecommerce communities [Extended Abstract]
Proceedings of the 4th ACM conference on Electronic commerce
Building trust in online auction markets through an economic incentive mechanism
Decision Support Systems
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
New algorithms for mining the reputation of participants of online auctions
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Intelligent reputation assessment for participants of web-based customer-to-customer auctions
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Data & Knowledge Engineering
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
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The expansion of the share of online auctions in electronic trade causes exponential growth of theft and deception associated with this retail channel. Trustworthy reputation systems are a crucial factor in fighting dishonest and malicious users. Unfortunately, popular online auction sites use only simple reputation systems that are easy to deceive, thus offering users little protection against organized fraud. In this paper we present a new reputation measure that is based on the notion of the density of sellers. Our measure uses the topology of connections between sellers and buyers to derive knowledge about trustworthy sellers. We mine the data on past transactions to discover clusters of interconnected sellers, and for each seller we measure the density of the seller’s neighborhood. We use discovered clusters both for scoring the reputation of individual sellers, and to assist buyers in informed decision making by generating automatic recommendations. We perform experiments on data acquired from a leading Polish provider of online auctions to examine the properties of discovered clusters. The results of conducted experiments validate the assumptions behind the density reputation measure and provide an interesting insight into clusters of dense sellers.