An AP-Based Shilling Attack Detector for Collaborative Recommender Systems

Abstract

Collaborative filtering is a technique widely used in online recommender systems nowadays. However, it is vulnerable from manipulation by malicious users who often create some fake account (or shilling) profiles to influence the results of recommender systems. To identify the fake users, existing algorithms usually utilize certain characteristics of shilling profiles, of which the drawback is the low precision and the requirement of a large size of training set. In this paper, we develop a clustering based method to find the shilling attackers by incorporating the information of user ratings and the attribute of user profiles. The users are firstly self-organizedly clustered into several groups based on the integrated information of the rating features and the attributes of user profile, then the malicious user group is identified through the GRDMA values of user group. Instead of identifying attacker one by one, the proposed algorithm finds the malicious users at the collective level, which providing a novel way to analyze and detect shilling attack. The experimental results performed on MovieLens datasets demonstrate that the proposed algorithm is effective and robust in three typical kinds of shilling attack models, especially when the attack size and the filler size are sufficiently high.