Classification features for attack detection in collaborative recommender systems

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“Classification features for attack detection in collaborative recommender systems” by Robin Burke, Bamshad Mobasher, Chad Williams, and Runa Bhaumik. In KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, (New York, NY, USA), 2006, pp. 542-547.

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.

Keywords: collaborative filtering, recommender systems, robustness, attack detection

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BibTeX entry:

@inproceedings{BMWB06,
   author = {Robin Burke and Bamshad Mobasher and Chad Williams and Runa
	Bhaumik},
   title = {Classification features for attack detection in collaborative
	recommender systems},
   booktitle = {KDD '06: Proceedings of the 12th ACM SIGKDD international
	conference on Knowledge discovery and data mining},
   pages = {542--547},
   publisher = {ACM},
   address = {New York, NY, USA},
   year = {2006},
   isbn = {1-59593-339-5},
   url = {http://doi.acm.org/10.1145/1150402.1150465}
}