Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Communications of the ACM
Collaborative reputation mechanisms for electronic marketplaces
Decision Support Systems - Special issue for business to business electronic commerce, issues and solutions
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Trusted intermediating agents in electronic trade networks
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
The Role of Reputation Systems in Reducing On-Line Auction Fraud
International Journal of Electronic Commerce
Reducing internet auction fraud
Communications of the ACM - Web searching in a multilingual world
Computational challenges in e-commerce
Communications of the ACM - Rural engineering development
Analyzing seller practices in a Brazilian marketplace
Proceedings of the 18th international conference on World wide web
Seller's credibility in electronic markets: a complex network based approach
Proceedings of the 3rd workshop on Information credibility on the web
Feature Extraction for Fraud Detection in Electronic Marketplaces
LA-WEB '09 Proceedings of the 2009 Latin American Web Congress (la-web 2009)
Fraud detection in reputation systems in e-markets using logistic regression
Proceedings of the 2010 ACM Symposium on Applied Computing
Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool
Expert Systems with Applications: An International Journal
A media-based social interactions analysis procedure
Proceedings of the 27th Annual ACM Symposium on Applied Computing
An effective early fraud detection method for online auctions
Electronic Commerce Research and Applications
Fraud detection in web transactions
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Risk analysis of electronic transactions in tourism web applications
Proceedings of the 19th Brazilian symposium on Multimedia and the web
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Reputation is the opinion of the public toward a person, a group of people, or an organization. Reputation systems are particularly important in e-markets, where they help buyers to decide whether to purchase a product or not. Since a higher reputation means more profit, some users try to deceive such systems to increase their reputation. E-markets should protect their reputation systems from attacks in order to maintain a sound environment. This work addresses the task of finding attempts to deceive reputation systems in e-markets. Our goal is to generate a list of users (sellers) ranked by the probability of fraud. Firstly we describe characteristics related to transactions that may indicate frauds evidence and they are expanded to the sellers. We describe results of a simple approach that ranks sellers by counting characteristics of fraud. Then we incorporate characteristics that cannot be used by the counting approach, and we apply logistic regression to both, improved and not improved. We use real data from a large Brazilian e-market to train and evaluate our methods and the improved set with logistic regression performs better, specially when we apply stepwise optimization. We validate our results with specialists of fraud detection in this market place. In the end, we increase by 112% the number of identified fraudsters against the reputation system. In terms of ranking, we reach 93% of average precision after specialists' review in the list that uses Logistic Regression and Stepwise optimization. We also detect 55% of fraudsters with a precision of 100%.