Manipulation Robustness of Collaborative Filtering

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 demonstrate that while nearest neighbor algorithms, which are widely used in commercial systems, are highly susceptible to manipulation, two classes of collaborative filtering algorithms which we refer to as linear and asymptotically linear are relatively robust.