Learning Fair Representations for Recommendation: A Graph-based Perspective-Reference-Cited by-同舟云学术

Learning Fair Representations for Recommendation: A Graph-based Perspective

Published:2021-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Web Conference 2021
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Author:

Wu Le¹,Chen Lei²,Shao Pengyang²,Hong Richang²,Wang Xiting³,Wang Meng¹

Affiliation:

1. Hefei University of Technology, China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China and Intelligent Interconnected Systems Laboratory of Anhui Province, China

2. Hefei University of Technology, China and Intelligent Interconnected Systems Laboratory of Anhui Province, China

3. Microsoft Research Asia, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3442381.3450015

Reference38 articles.

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2. Alex Beutel Jilin Chen Zhe Zhao and Ed H Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. In FAT/ML. Alex Beutel Jilin Chen Zhe Zhao and Ed H Chi. 2017. Data decisions and theoretical implications when adversarially learning fair representations. In FAT/ML.

3. Avishek Joey Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In ICML. 715–724. Avishek Joey Bose and William Hamilton. 2019. Compositional fairness constraints for graph embeddings. In ICML. 715–724.

4. Òscar Celma Herrada 2009. Music recommendation and discovery in the long tail. Universitat Pompeu Fabra. Òscar Celma Herrada 2009. Music recommendation and discovery in the long tail. Universitat Pompeu Fabra.

5. Paul Covington Jay Adams and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In RecSys. 191–198. Paul Covington Jay Adams and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In RecSys. 191–198.

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