document: 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 can not 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 .
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