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Manipulation Robustness of Collaborative Filtering

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.
Year of publication:
2010
Authors: Roy, Benjamin Van ; Yan, Xiang
Published in:
Management Science. - Institute for Operations Research and the Management Sciences - INFORMS, ISSN 0025-1909. - Vol. 56.2010, 11, p. 1911-1929
Publisher: Institute for Operations Research and the Management Sciences - INFORMS
Subject: enabling technologies (includes artificial intelligence | machine learning | and data mining technologies) | probability | stochastic model applications | statistics | nonparametric
Extent:
application/pdf
Type of publication: Article
Other identifiers:
10.1287/mnsc.1100.1232 [DOI]
Source:
Persistent link: https://www.econbiz.de/10009214872
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