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Manipulation Robustness of Collaborative Filtering

Author

Listed:
  • Benjamin Van Roy

    () (Stanford University, Stanford, California 94305)

  • Xiang Yan

    () (Stanford University, Stanford, California 94305)

Abstract

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.

Suggested Citation

  • Benjamin Van Roy & Xiang Yan, 2010. "Manipulation Robustness of Collaborative Filtering," Management Science, INFORMS, vol. 56(11), pages 1911-1929, November.
  • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:11:p:1911-1929
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    File URL: http://dx.doi.org/10.1287/mnsc.1100.1232
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    References listed on IDEAS

    as
    1. Nolan Miller & Paul Resnick & Richard Zeckhauser, 2005. "Eliciting Informative Feedback: The Peer-Prediction Method," Management Science, INFORMS, vol. 51(9), pages 1359-1373, September.
    2. Gossner, Olivier & Tomala, Tristan, 2008. "Entropy bounds on Bayesian learning," Journal of Mathematical Economics, Elsevier, vol. 44(1), pages 24-32, January.
    3. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    4. 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.
    5. repec:dau:papers:123456789/6067 is not listed on IDEAS
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