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ABSTRACT Classification Features for Attack Detection in Collaborative Recommender Systems ∗

By Robin Burke, Bamshad Mobasher, Chad Williams and Runa Bhaumik

Abstract

Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in recommendations that favor or disfavor given items. Since collaborative recommender systems must be open to user input, it is difficult to design a system that cannot be so attacked. Researchers studying robust recommendation have therefore begun to identify types of attacks and study mechanisms for recognizing and defeating them. In this paper, we propose and study different attributes derived from user profiles for their utility in attack detection. We show that a machine learning classification approach that includes attributes derived from attack models is more successful than more generalized detection algorithms previously studied

Topics: Intelligence, Learning General Terms Experimentation, Algorithms, Security Keywords collaborative filtering
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.4655
Provided by: CiteSeerX
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