Towards robust collaborative filtering

Collaborative filtering has now become a popular choice for reducing information overload. While many researchers have proposed and compared the performance of various collaborative filtering algorithms, one important performance measure has been omitted from the research to date. Robustness measures the power of an algorithm to make good predictions in the presence of erroneous data. In this paper, we argue that robustness is an important system characteristic, and that it must be considered from the point-of-view of potential attacks that could be made on the system by malicious users.