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Manipulation Robustness of Collaborative Filtering

Author

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  • 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
    DOI: 10.1287/mnsc.1100.1232
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    References listed on IDEAS

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    Cited by:

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