Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
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
ACM Transactions on Information Systems (TOIS)
The sound of silence: mining implicit feedbacks to compute reputation
WINE'06 Proceedings of the Second international conference on Internet and Network Economics
Cluster-Based analysis and recommendation of sellers in online auctions
TrustBus'06 Proceedings of the Third international conference on Trust, Privacy, and Security in Digital Business
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The assessment of credibility and reputation of contractors in online auctions is the key issue in providing reliable environment for customer-to-customer e-commerce. Confident reputation rating system is an important factor in managing risk and building customer satisfaction. Unfortunately, most online auction sites employ a very simple reputation rating scheme that utilizes user feedbacks and comments issued after committed auctions. Such schemes are easy to deceive and do not provide satisfactory protection against several types of fraud. In this paper we propose two novel measures of trustworthiness, namely, credibility and density. We draw inspiration from social network analysis and present two algorithms for reputation rating estimation. Our first algorithm computes the credibility of participants by an iterative search of inter-participant connections. Our second algorithm discovers clusters of participants who are densely connected through committed auctions. We test both measures on a large body of real-world data and we experimentally compare them with existing solutions.