Manipulation-resistant collaborative filtering systems

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Manipulation-resistant collaborative filtering systems

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ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents

New York, New York, USA

SESSION: Privacy and security table of contents

Pages 165-172

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Benjamin Van Roy	Stanford University, Stanford, USA
Xiang Yan	Stanford University, Stanford, USA

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

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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, a class of collaborative filtering algorithms which we refer to as linear is relatively robust. These results provide guidance for the design of future collaborative filtering systems.

REFERENCES

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