Manipulation Robustness of Collaborative Filtering
AbstractA 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.
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Bibliographic InfoArticle provided by INFORMS in its journal Management Science.
Volume (Year): 56 (2010)
Issue (Month): 11 (November)
enabling technologies (includes artificial intelligence; machine learning; and data mining technologies); probability; stochastic model applications; statistics; nonparametric;
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