Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
Proceedings of the 10th international conference on Intelligent user interfaces
Is trust robust?: an analysis of trust-based recommendation
Proceedings of the 11th international conference on Intelligent user interfaces
A survey of trust and reputation systems for online service provision
Decision Support Systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Smart cheaters do prosper: defeating trust and reputation systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A survey of attack and defense techniques for reputation systems
ACM Computing Surveys (CSUR)
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Distortion as a validation criterion in the identification of suspicious reviews
Proceedings of the First Workshop on Social Media Analytics
A Fine-Grained Reputation System for Reliable Service Selection in Peer-to-Peer Networks
IEEE Transactions on Parallel and Distributed Systems
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We propose a lightweight reputation model R-Rep, for resisting manipulative behavior in Reputation Systems. We present a manipulative behavior detection system CSI to detect the customers who provide manipulative ratings, and the vendors who intend to increase their reputation value in a strategic manner. Via analysing motivation of manipulative behavior, we specify features for identifying suspicious customers using clustering algorithm. Utilizing the inherent relationship between suspicious customers and suspicious vendors, the first set of suspicious vendors is identified by CSI. Meanwhile, using different pieces of information, which refer to non-anonymous ratings and anonymous ratings, the second and the third sets of suspicious vendors are detected by CSI. We designed two universal approaches RVA and BVA to compare different reputation models with regard to resisting manipulative behavior. The comparison approaches are applied to a set of suspicious vendors identified by CSI. Results show that, R-Rep outperforms two existing models, the reputation model employed by Taobao (the largest e-commerce site in China) and a Bayesian System. The two comparing approaches RVA and BVA have inherent consistency.