NSPW '97 Proceedings of the 1997 workshop on New security paradigms
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Managing trust in a peer-2-peer information system
Proceedings of the tenth international conference on Information and knowledge management
Supporting Fine-grained Data Lineage in a Database Visualization Environment
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
TrustGuard: countering vulnerabilities in reputation management for decentralized overlay networks
WWW '05 Proceedings of the 14th international conference on World Wide Web
Propagation Models for Trust and Distrust in Social Networks
Information Systems Frontiers
Topical TrustRank: using topicality to combat web spam
Proceedings of the 15th international conference on World Wide Web
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Gradual trust and distrust in recommender systems
Fuzzy Sets and Systems
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Despite its success, similarity-based collaborative filtering suffers from some limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system. To the best of our knowledge, there has not any prior study on recommendation attack in a trust-based recommender system. We analyze the attack problem, and find that "victim" nodes play a significant role in the attack. Furthermore, we propose a data provenance method to trace malicious users and identify the "victim" nodes as distrust users of recommender system. Feasibility study of the defend method is done with the dataset crawled from Epinions website.