Manipulation Robustness of Collaborative Filtering Systems

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dc.creatorYan, Xiang
dc.creatorVan Roy, Benjamin
dc.date2009-02-28
dc.date2009-04-19
dc.date.accessioned2026-07-07T13:05:22Z
dc.date.available2026-07-07T13:05:22Z
dc.descriptionA 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.
dc.identifierhttps://arxiv.org/abs/0903.0064
dc.identifierhttp://arxiv.org/abs/0903.0064
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/227466
dc.subjectMachine Learning
dc.subjectInformation Theory
dc.titleManipulation Robustness of Collaborative Filtering Systems
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