Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
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
Proceedings of the 2007 ACM conference on Recommender systems
ACM Conference on Recommender Systems
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Robustness of collaborative recommendation based on association rule mining
Proceedings of the 2007 ACM conference on Recommender systems
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Cross-representation mediation of user models
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Proceedings of the third ACM conference on Recommender systems
Robustness of recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Semi-SAD: applying semi-supervised learning to shilling attack detection
Proceedings of the fifth ACM conference on Recommender systems
A user trust-based collaborative filtering recommendation algorithm
ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting and identifying coalitions
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A hybrid decision approach to detect profile injection attacks in collaborative recommender systems
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
βP: A novel approach to filter out malicious rating profiles from recommender systems
Decision Support Systems
A unified framework for reputation estimation in online rating systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Collaborative filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation by malicious social elements. Lies and Propaganda may be spread by a malicious user who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious user votes and profiles can be injected into a collaborative recommender system. This can significantly affect the robustness of a system or algorithm, as has been studied in previous work. While current detection algorithms are able to use certain characteristics of shilling profiles to detect them, they suffer from low precision, and require a large amount of training data. In this work, we provide an in-depth analysis of shilling profiles and describe new approaches to detect malicious collaborative filtering profiles. In particular, we exploit the similarity structure in shilling user profiles to separate them from normal user profiles using unsupervised dimensionality reduction. We present two detection algorithms; one based on PCA, while the other uses PLSA. Experimental results show a much improved detection precision over existing methods without the usage of additional training time required for supervised approaches. Finally, we present a novel and highly effective robust collaborative filtering algorithm which uses ideas presented in the detection algorithms using principal component analysis.