Manipulation-resistant collaborative filtering systems
Benjamin Van Roy, Xiang “Jacob” Yan · 2009
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.