GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Privacy Risks in Recommender Systems
IEEE Internet Computing
Towards Robust Collaborative Filtering
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Proceedings of the third ACM conference on Recommender systems
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
Robustness of recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Stability of Recommendation Algorithms
ACM Transactions on Information Systems (TOIS)
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Personalisation features are key to the success of many web applications and collaborative recommender systems have been widely implemented. These systems assist users in finding relevant information or products from the vast quantities that are frequently available. In previous work, we have demonstrated that such systems are vulnerable to attack and that recommendations can be manipulated. We introduced the concept of robustness as a performance measure, which is defined as the ability of a system to provide consistent predictions in the presence of noise in the data. In this paper, we expand on our previous work by examining the effects of several neighbourhood formation schemes and similarity measures on system performance. We propose a neighbourhood filtering mechanism for filtering false profiles from the neighbourhood in order to improve the robustness of the system.