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Manipulation Robustness of Collaborative Filtering Systems

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    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 provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, two classes of collaborative filtering algorithms which we refer to as linear and asymptotically linear are relatively robust. These results provide guidance for the design of future collaborative filtering systems.

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    File URL: http://www.netinst.org/Van-Roy_Yan_09-21.pdf
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    Bibliographic Info

    Paper provided by NET Institute in its series Working Papers with number 09-21.

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    Length: 40 pages
    Date of creation: Sep 2009
    Date of revision: Sep 2009
    Handle: RePEc:net:wpaper:0921

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    Web page: http://www.NETinst.org/

    Related research

    Keywords: recommendation system; collaborative filtering; manipulation; information theory; statistics;

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