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“The Impact of Attack Profile Classification on the Robustness of Collaborative Recommendation” by Chad Williams, Runa Bhaumik, Robin Burke, and Bamshad Mobasher. In Proceedings of the 2006 WebKDD Workshop, (Held at KDD 2006, Philadelphia), Aug. 2006.
Collaborative recommender systems have been shown to be vulnerable to profile injection attacks. By injecting a large number of biased profiles into a system, attackers can manipulate the predictions of targeted items. To decrease this risk, researchers have begun to study mechanisms for detecting and preventing profile injection attacks. In prior work, we proposed several attributes for attack detection and have shown that a classifier built with them can be highly successful at identifying attack profiles. In this paper, we extend our work through a more detailed analysis of the information gain associated with these attributes across the dimensions of attack type and profile size. We then evaluate their combined effectiveness at improving the robustness of user based recommender systems.
Keywords: attack detection, attack models, bias profile injection, collaborative filtering, pattern recognition, profile classification, recommender systems, shilling
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BibTeX entry:
@inproceedings{WBRM06,
author = {Chad Williams and Runa Bhaumik and Robin Burke and Bamshad
Mobasher},
title = {The Impact of Attack Profile Classification on the Robustness
of Collaborative Recommendation},
booktitle = {Proceedings of the 2006 WebKDD Workshop},
address = {Held at KDD 2006, Philadelphia},
month = aug,
year = {2006},
url = {http://webmining.spd.louisville.edu/webkdd06/accepted.html}
}