Manipulation Robustness of Collaborative Filtering
A collaborative filtering system recommends to users products that similar users like. Collaborative filtering systems influence purchase decisions and hence have become targets of manipulation by unscrupulous vendors. We demonstrate that nearest neighbors algorithms, which are widely used in commercial systems, are highly susceptible to manipulation and introduce new collaborative filtering algorithms that are relatively robust.
Volume (Year): 56 (2010)
Issue (Month): 11 (November)
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- Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
- Tristan Tomala & Olivier Gossner, 2008. "Entropy bounds on Bayesian learning," Post-Print hal-00464554, HAL.
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- Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
- Gossner, Olivier & Tomala, Tristan, 2008. "Entropy bounds on Bayesian learning," Journal of Mathematical Economics, Elsevier, vol. 44(1), pages 24-32, January.
- Olivier Gossner & Tristan Tomala, 2008. "Entropy bounds on Bayesian learning," Post-Print halshs-00754314, HAL.
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