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Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation

  • Bamshad Mobasher
  • Robin Burke
  • Chad Williams
  • Runa Bhaumik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4198)

Abstract

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 can easily introduce biased data in an attempt to force the system to “adapt” in a manner advantageous to them. Our research in secure personalization is examining a range of attack models, from the simple to the complex, and a variety of recommendation techniques. In this chapter, we explore an attack model that focuses on a subset of users with similar tastes and show that such an attack can be highly successful against both user-based and item-based collaborative filtering. We also introduce a detection model that can significantly decrease the impact of this attack.

Keywords

Recommender System Target Item Recommendation Algorithm Attack Model Average Attack 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bamshad Mobasher
    • 1
  • Robin Burke
    • 1
  • Chad Williams
    • 1
  • Runa Bhaumik
    • 1
  1. 1.Center for Web Intelligence, School of Computer Science, Telecommunication and Information SystemsDePaul UniversityChicago

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