Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems

@inproceedings{Mobasher2005EffectiveAM,
  title={Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems},
  author={Bamshad Mobasher and R. Burke and Runa Bhaumik and Chad Williams},
  year={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… CONTINUE READING
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