Abstract
Collaborative filtering has nowb ecome 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-viewof potential attacks that could be made on the system by malicious users.
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References
J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, pages 43–52, July 1998.
D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61–70, December 1992.
P. Groot, F. van Harmelen, and A. ten Teije. Torture tests: a quantitative analysis for the robustness of knowledge-based systems. In European Workshop on Knowledge Acquisition, Modelling and Management (EKAW’00). LNAI Springer-Verlag, October 2000.
C.-N. Hsu and C. A. Knoblock. Estimating the robustness of discovered knowledge. In Proceedings of the First International Conference on Knwledge Discovery and Data Mining, Montreal, Canada, 1995.
C.-N. Hsu and C. A. Knoblock. Discovering robust knowledge from dynamic closedworld data. In Proceedings of AAAI’96, 1996.
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. An open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work. ACM, 1994.
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O’Mahony, M.P., Hurley, N.J., Silvestre, G.C.M. (2002). Towards Robust Collaborative Filtering. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science(), vol 2464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45750-X_11
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DOI: https://doi.org/10.1007/3-540-45750-X_11
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