A differential privacy framework for matrix factorization recommender systems

User Model. User-Adapt. Interact., Volume 26, Issue 5, 2016, Pages 425-458.

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Abstract:

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 p...More

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