Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
C4.5: programs for machine learning
C4.5: programs for machine learning
Communications of the ACM
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Proceedings of the 10th international conference on Intelligent user interfaces
Preventing shilling attacks in online recommender systems
Proceedings of the 7th annual ACM international workshop on Web information and data management
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Analysis of social voting patterns on digg
Proceedings of the first workshop on Online social networks
Trust Is in the Eye of the Beholder
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Some thoughts on using argumentation to handle trust
CLIMA'11 Proceedings of the 12th international conference on Computational logic in multi-agent systems
Using argumentation to reason with and about trust
ArgMAS'11 Proceedings of the 8th international conference on Argumentation in Multi-Agent Systems
Defending recommender systems by influence analysis
Information Retrieval
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Online recommender systems are a common target of attack. Existing research has focused on automated manipulation of recommender systems through the creation of shill accounts, and either do not consider attacks by coalitions of real users, downplay the impact of such attacks, or state that such attacks are difficult to impossible to detect. In this study, we examine a recommender system that is part of an online social network, show that users successfully induced other users to manipulate their recommendations, that these manipulations were effective, and that most such manipulations are detectable even when performed by ordinary, non-automated users.