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Segment-based injection attacks against collaborative filtering recommender systems

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4 Author(s)
R. Burke ; Center for Web Intelligence, DePaul Univ., Chicago, IL, USA ; B. Mobasher ; R. Bhaumik ; C. Williams

Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Researchers have shown that attackers can manipulate a system's recommendations by injecting biased profiles into it. In this paper, we examine attacks that concentrate on a targeted set of users with similar tastes, biasing the system's responses to these users. We show that such attacks are both pragmatically reasonable and also highly effective against both user-based and item-based algorithms. As a result, an attacker can mount such a "segmented" attack with little knowledge of the specific system being targeted and with strong likelihood of success.

Published in:

Fifth IEEE International Conference on Data Mining (ICDM'05)

Date of Conference:

27-30 Nov. 2005