FairRec: Fairness Testing for Deep Recommender Systems-Reference-Cited by-同舟云学术

FairRec: Fairness Testing for Deep Recommender Systems

Published:2023-07-12 Issue: Volume: Page:
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Container-title:Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis
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Author:

Guo Huizhong¹,Li Jinfeng²,Wang Jingyi¹,Liu Xiangyu²,Wang Dongxia¹,Hu Zehong²,Zhang Rong²,Xue Hui²

Affiliation:

1. Zhejiang University, China

2. Alibaba Group, China

Funder

Key R\&D Program of Zhejiang

NSFC Programs

Alibaba Group through Alibaba Innovative Research Program

the Research Center of Alibaba Artificial Intelligence Governance

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597926.3598058

Reference50 articles.

1. Himan Abdollahpouri and Robin Burke. 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:1907.13158. Himan Abdollahpouri and Robin Burke. 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:1907.13158.

2. User-centered Evaluation of Popularity Bias in Recommender Systems

3. Black box fairness testing of machine learning models

4. Themis: automatically testing software for discrimination

5. Fairness in Recommendation Ranking through Pairwise Comparisons

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