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Manipulation Robustness of Collaborative Filtering Systems

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.

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File URL: http://www.netinst.org/Van-Roy_Yan_09-21.pdf
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Paper provided by NET Institute in its series Working Papers with number 09-21.

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Length: 40 pages
Date of creation: Sep 2009
Date of revision: Sep 2009
Handle: RePEc:net:wpaper:0921
Contact details of provider: Web page: http://www.NETinst.org/

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  1. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
  2. Nolan Miller & Paul Resnick & Richard Zeckhauser, 2005. "Eliciting Informative Feedback: The Peer-Prediction Method," Management Science, INFORMS, vol. 51(9), pages 1359-1373, September.
  3. Gossner, Olivier & Tomala, Tristan, 2008. "Entropy bounds on Bayesian learning," Journal of Mathematical Economics, Elsevier, vol. 44(1), pages 24-32, January.
  4. Sangkil Moon & Gary J. Russell, 2008. "Predicting Product Purchase from Inferred Customer Similarity: An Autologistic Model Approach," Management Science, INFORMS, vol. 54(1), pages 71-82, January.
  5. Gossner, Olivier & Tomala, Tristan, 2008. "Entropy Bounds on Bayesian Learning," Economics Papers from University Paris Dauphine 123456789/6067, Paris Dauphine University.
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