Maximum Entropy Policy for Long-Term Fairness in Interactive Recommender Systems-Reference-Cited by-同舟云学术

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Maximum Entropy Policy for Long-Term Fairness in Interactive Recommender Systems

Published:2024-05 Issue:3 Volume:17 Page:1029-1043
ISSN:1939-1374
Container-title:IEEE Transactions on Services Computing
language:
Short-container-title:IEEE Trans. Serv. Comput.

Author:

Shi Xiaoyu¹^ORCID,Liu Quanliang¹^ORCID,Xie Hong¹^ORCID,Bai Yanan²^ORCID,Shang Mingsheng¹^ORCID

Affiliation:

1. Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China

2. School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4629386/10554674/10403940.pdf?arnumber=10403940

Reference44 articles.

1. Learning to context-aware recommend with hierarchical factorization machines

2. Feedback Loop and Bias Amplification in Recommender Systems

3. Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems

4. Recommendations as treatments: Debiasing learning and evaluation;Schnabel

5. Building Classifiers with Independency Constraints

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. AI alignment: Assessing the global impact of recommender systems;Futures;2024-06

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