GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
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)
Preventing shilling attacks in online recommender systems
Proceedings of the 7th annual ACM international workshop on Web information and data management
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
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
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Unsupervised shilling detection for collaborative filtering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Analysis and detection of segment-focused attacks against collaborative recommendation
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
BlurMe: inferring and obfuscating user gender based on ratings
Proceedings of the sixth ACM conference on Recommender systems
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Robustness analysis research has shown that conventional memory-based recommender systems are very susceptible to malicious profile-injection attacks. A number of attack models have been proposed and studied and recent work has suggested that model-based collaborative filtering (CF) algorithms have greater robustness against these attacks. Moreover, to combat such attacks, several attack detection algorithms have been proposed. One that has shown high detection accuracy is based on using principal component analysis (PCA) to cluster attack profiles on the basis that such profiles are highly correlated. In this paper, we argue that the robustness observed in model-based algorithms is due to the fact that the proposed attacks have not targeted the specific vulnerabilities of these algorithms. We discuss how an effective attack targeting model-based algorithms that employ profile clustering can be designed. It transpires that the attack profiles employed in this attack, exhibit low rather than high pair-wise similarities and can easily be obfuscated to avoid PCA-based detection, while remaining effective.