Evil Twins: Modeling Power Users in Attacks on Recommender Systems

@inproceedings{Wilson2014EvilTM,
  title={Evil Twins: Modeling Power Users in Attacks on Recommender Systems},
  author={David C. Wilson and Carlos E. Seminario},
  booktitle={UMAP},
  year={2014}
}
Attacks on Collaborative Filtering Recommender Systems (RS) can bias recommendations, potentially causing users to distrust results and the overall system. Attackers constantly innovate, and understanding the implications of novel attack vectors on system robustness is important for designers and operators. Foundational research on attacks in RSs studied attack user profiles based on straightforward models such as random or average ratings data. We are studying a novel category of attack based… CONTINUE READING

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