research-article

Fairness Metrics for Recommender Systems

Author:

Hao WangAuthors Info & Claims

icWCSN '22: Proceedings of the 2022 9th International Conference on Wireless Communication and Sensor Networks

Pages 89 - 92

https://doi.org/10.1145/3514105.3514120

Published: 01 April 2022 Publication History

Abstract

Fairness is a hot topic in recommender system research in recent years. Researchers have resorted to regularization and other techniques to reduce fairness problems. However, a lot of research literature adopts classic evaluation metrics for recommender system results. There has been little attention paid to the fairness metrics for recommender system evaluation. In this paper and for the first time in the research history of recommender systems, we propose a set of fairness metrics based on extreme value theory. In the experiment section, we evaluate different classic algorithms and fair AI technologies with our newly invented fairness metrics.

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Cited By

Wang H(2023)LogitMat: Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA56900.2023.10154697(138-142)Online publication date: 26-Apr-2023
https://doi.org/10.1109/ICCCBDA56900.2023.10154697

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icWCSN '22: Proceedings of the 2022 9th International Conference on Wireless Communication and Sensor Networks

January 2022

159 pages

ISBN:9781450396219

DOI:10.1145/3514105

Copyright © 2022 ACM.

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Publication History

Published: 01 April 2022

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icWCSN 2022

icWCSN 2022: 2022 9th international Conference on Wireless Communication and Sensor Networks

January 11 - 13, 2022

Dalian, China

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Cited By

Wang H(2023)LogitMat: Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA56900.2023.10154697(138-142)Online publication date: 26-Apr-2023
https://doi.org/10.1109/ICCCBDA56900.2023.10154697

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