Manipulation Robustness of Collaborative Filtering

Benjamin Van Roy () and Xiang Yan ()
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Benjamin Van Roy: Stanford University, Stanford, California 94305
Xiang Yan: Stanford University, Stanford, California 94305

Management Science, 2010, vol. 56, issue 11, 1911-1929

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.

Keywords: enabling technologies (includes artificial intelligence; machine learning; and data mining technologies); probability; stochastic model applications; statistics; nonparametric (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:56:y:2010:i:11:p:1911-1929

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