Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems

Evaluating, Understanding, and Mitigating Unfairness in Recommender Systems

Recommender systems are information filtering tools that discover potential matchings between users and items and benefit both parties. This benefit can be considered a social resource that should be equitably allocated across users and items, especially in critical domains such as education and emp...

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Bibliographic Details
Main Author: Yao, Sirui
Other Authors: Computer Science
Format: Others
Published: Virginia Tech 2021
Subjects:
Algorithmic Fairness
Recommender Systems
Matrix Factorization
Personalized Regularization
Long-term Equality
Online Access:http://hdl.handle.net/10919/103779

Internet

http://hdl.handle.net/10919/103779

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