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“Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems” by B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. In Proceedings of the 2005 WebKDD Workshop, (Held at KDD 2005, Chicago, Illinois), Aug. 2005.
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. These vulnerabilities mostly emanate from the open nature of such systems and their reliance on user-specified judgments for building profiles. Attackers who cannot be readily distinguished from ordinary users may introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. A handful of simple attack models have, so far, been identified, and there appear to be significant differences in the susceptibility of different recommendation techniques to these attacks. In particular, item-based collaborative filtering has been found to offer some security advantages over user-based collaborative filtering. Our research in secure personalization is examining a range of more complex attack models and recommendation techniques, paying particular attention to the costs and benefits of mounting an attack. In this paper, we take a closer look at item-based collaborative filtering. In particular, we propose a new attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against an item-based algorithm.
Keywords: shilling, collaborative filtering, recommender systems, attack models, item-based
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BibTeX entry:
@inproceedings{MBBW05,
author = {B. Mobasher and R. Burke and R. Bhaumik and C. Williams},
title = {Effective Attack Models for Shilling Item-Based Collaborative
Filtering Systems},
booktitle = {Proceedings of the 2005 WebKDD Workshop},
address = {Held at KDD 2005, Chicago, Illinois},
month = aug,
year = {2005},
url = {http://db.cs.ualberta.ca/webkdd05/proceedings.pdf}
}