The Stereotyping Problem in Collaboratively Filtered Recommender Systems

@article{Guo2021TheSP,
  title={The Stereotyping Problem in Collaboratively Filtered Recommender Systems},
  author={Wenshuo Guo and Karl Krauth and Michael I. Jordan and Nikhil Garg},
  journal={Equity and Access in Algorithms, Mechanisms, and Optimization},
  year={2021}
}
  • Wenshuo Guo, K. Krauth, N. Garg
  • Published 23 June 2021
  • Computer Science
  • Equity and Access in Algorithms, Mechanisms, and Optimization
Recommender systems play a crucial role in mediating our access to online information. We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the general user population, then those items may not be recommended together to a user, regardless of that user’s preferences and rating history. First, we introduce a notion of joint accessibility, which measures the extent to which a set of items can jointly be accessed by users… 
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