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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.

Suggested Citation

  • Benjamin Van Roy & Xiang Yan, 2009. "Manipulation Robustness of Collaborative Filtering Systems," Working Papers 09-21, NET Institute, revised Sep 2009.
    • Handle: RePEc:net:wpaper:0921
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    More about this item

    Keywords

    recommendation system; collaborative filtering; manipulation; information theory; statistics;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    Statistics

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