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2006
ACM

Classification features for attack detection in collaborative recommender systems

11 years 16 hour ago
Classification features for attack detection in collaborative recommender systems
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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval; H.3.4 [Information Storage and Retrieval]: Systems and Software...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2006
Where KDD
Authors Robin D. Burke, Bamshad Mobasher, Chad Williams, Runa Bhaumik
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