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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 anddoi:10.1145/1150402.1150465 dblp:conf/kdd/BurkeMWB06 fatcat:zkk4o2pumbfsbpqpit2uv3fwdm