GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Studying Recommendation Algorithms by Graph Analysis
Journal of Intelligent Information Systems
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Attacking Recommender Systems: A Cost-Benefit Analysis
IEEE Intelligent Systems
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Unsupervised retrieval of attack profiles in collaborative recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative web search: a robustness analysis
Artificial Intelligence Review
Effective diverse and obfuscated attacks on model-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Merging multiple criteria to identify suspicious reviews
Proceedings of the fourth ACM conference on Recommender systems
Analysis of robustness in trust-based recommender systems
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Robustness analysis of model-based collaborative filtering systems
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Distortion as a validation criterion in the identification of suspicious reviews
Proceedings of the First Workshop on Social Media Analytics
Robustness of recommender systems
Proceedings of the fifth ACM conference on Recommender systems
BlurMe: inferring and obfuscating user gender based on ratings
Proceedings of the sixth ACM conference on Recommender systems
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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In the research to date, the performance of recommender systems has been extensively evaluated across various dimensions. Increasingly, the issue of robustness against malicious attack is receiving attention from the research community. In previous work, we have shown that knowledge of certain domain statistics is sufficient to allow successful attacks to be mounted against recommender systems. In this paper, we examine the extent of domain knowledge that is actually required and find that, even when little such knowledge is known, it remains possible to mount successful attacks.