Manipulation Robustness of Collaborative Filtering Systems

Benjamin Van Roy () and Xiang Yan ()
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Benjamin Van Roy: Stanford University, http://www.stanford.edu/~bvr
Xiang Yan: Stanford University

No 09-21, Working Papers from NET Institute

Abstract: 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.

Keywords: recommendation system; collaborative filtering; manipulation; information theory; statistics (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2009-09, Revised 2009-09
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