Robustness analysis of model-based collaborative filtering systems

Abstract

Collaborative filtering (CF) recommender systems are very popular and successful in commercial application fields. However, robustness analysis research has shown that conventional memory-based recommender systems are very susceptible to malicious profile-injection attacks. A number of attack models have been proposed and studied and recent work has suggested that model-based CF algorithms have greater robustness against these attacks. In this paper, we argue that the robustness observed in model-based algorithms is due to the fact that the proposed attacks have not targeted the specific vulnerabilities of these algorithms. We discuss how effective attacks targeting factor analysis CF algorithm and k-means CF algorithm that employ profile modeling can be designed. It transpires that the attack profiles employed in these attacks, exhibit better performance than the traditional attacks. © 2010 Springer-Verlag.