Privacy for Recommender Systems

by Bart Knijnenburg (Clemson University, USA) and Shlomo Berkovsky (CSIRO, Australia)

Websites increasingly gather tremendous amounts of user data for recommendation purposes. This data may pose a severe threat to user privacy, e.g., if accessed by untrusted parties, or used inappropriately. Hence, it important for recommender system designers and service providers to learn about ways to generate accurate recommendations while at the same time respecting the privacy of their users. In this tutorial, we will:

  • analyze common privacy risks imposed by recommender systems
  • survey architectural, algorithmic, policy-related, and UI-design solutions
  • discuss implications for users

This tutorial is of general interest and is relevant for both participants with longstanding experience in recommender systems, as well as to newcomers. No specific background or skills are required. The tutorial will conclude with a plenary discussion of the future of privacy in recommender systems.




Monday, Aug 28, 2017, 16:15-18:00


Main Room

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