Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Torture Tests: A Quantitative Analysis for the Robustness of Knowledge-Based Systems
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering
Artificial Intelligence Review
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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Collaborative filtering has now become a popular choice for reducing information overload. While many researchers have proposed and compared the performance of various collaborative filtering algorithms, one important performance measure has been omitted from the research to date. Robustness measures the power of an algorithm to make good predictions in the presence of erroneous data. In this paper, we argue that robustness is an important system characteristic, and that it must be considered from the point-of-view of potential attacks that could be made on the system by malicious users.