Manipulation Robustness of Collaborative Filtering Systems
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 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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Bibliographic InfoPaper provided by NET Institute in its series Working Papers with number 09-21.
Length: 40 pages
Date of creation: Sep 2009
Date of revision: Sep 2009
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Web page: http://www.NETinst.org/
recommendation system; collaborative filtering; manipulation; information theory; statistics;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-10-24 (All new papers)
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