Detecting Shilling Attacks in Recommender Systems Based on Non-random-missing Mechanism

Abstract

Collaborative filtering recommender systems are highly vulnerable to shilling attacks. Developing detection techniques against shilling attacks has become the key to guaranteeing both the reliability and robustness of recommender systems. Through revealing the latent factors invoking missing ratings under the non-random-missing mechanism, and further combining these latent factors with Dirichlet process in the framework of probabilistic generative model, this paper proposes a latent factor analysis for missing ratings (LFAMR) model for attack detection. Experimental results show that comparing with the existing detection techniques, LFAMR is more universal and unsupervised, and that it can effectively detect shilling attacks of typical types even in lack of system-related prior knowledge.