A differential privacy framework for matrix factorization recommender systems

@article{Friedman2016ADP,
  title={A differential privacy framework for matrix factorization recommender systems},
  author={Arik Friedman and Shlomo Berkovsky and Mohamed Ali K{\^a}afar},
  journal={User Modeling and User-Adapted Interaction},
  year={2016},
  volume={26},
  pages={425-458}
}
Recommender systems rely on personal information about user behavior for the recommendation generation purposes. Thus, they inherently have the potential to hamper user privacy and disclose sensitive information. Several works studied how neighborhood-based recommendation methods can incorporate user privacy protection. However, privacy preserving latent factor models, in particular, those represented by matrix factorization techniques, the state-of-the-art in recommender systems, have received… CONTINUE READING
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