Skip navigation

Manipulation Robustness of Collaborative Filtering Systems

Authors: Van Roy, Benjamin - Stanford University
Yan, Xiang - Stanford University
Issue Date: 2009
Series/Report no.: Net Institute Working Paper;09-21
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.
Appears in Collections:NET Institute Working Papers Series

Files in This Item:
File Description SizeFormat 
Van-Roy_Yan_09-21.pdf358.29 kBAdobe PDFView/Open

Items in FDA are protected by copyright, with all rights reserved, unless otherwise indicated.