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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 5: AAAI-21 Technical Tracks 5

Fairness-aware News Recommendation with Decomposed Adversarial Learning

February 1, 2023

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Authors

Chuhan Wu

Department of Electronic Engineering & BNRist, Tsinghua University


Fangzhao Wu

Microsoft Research Asia


Xiting Wang

Microsoft Research Asia


Yongfeng Huang

Department of Electronic Engineering & BNRist, Tsinghua University


Xing Xie

Microsoft Research Asia


DOI:

10.1609/aaai.v35i5.16573


Abstract:

News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users with the same sensitive attributes (e.g., genders) have similar patterns and news recommendation models can easily capture these patterns. It may lead to some biases related to sensitive user attributes in the recommendation results, e.g., always recommending sports news to male users, which is unfair since users may not receive diverse news information. In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes. In our approach, we propose to decompose the user interest model into two components. One component aims to learn a bias-aware user embedding that captures the bias information on sensitive user attributes, and the other aims to learn a bias-free user embedding that only encodes attribute-independent user interest information for fairness-aware news recommendation. In addition, we propose to apply an attribute prediction task to the bias-aware user embedding to enhance its ability on bias modeling, and we apply adversarial learning to the bias-free user embedding to remove the bias information from it. Moreover, we propose an orthogonality regularization method to encourage the bias-free user embeddings to be orthogonal to the bias-aware one to better distinguish the bias-free user embedding from the bias-aware one. For fairness-aware news ranking, we only use the bias-free user embedding. Extensive experiments on benchmark dataset show that our approach can effectively improve fairness in news recommendation with minor performance loss.

Topics: AAAI

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HOW TO CITE:

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie Fairness-aware News Recommendation with Decomposed Adversarial Learning Proceedings of the AAAI Conference on Artificial Intelligence (2021) 4462-4469.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie Fairness-aware News Recommendation with Decomposed Adversarial Learning AAAI 2021, 4462-4469.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie (2021). Fairness-aware News Recommendation with Decomposed Adversarial Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 4462-4469.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie. Fairness-aware News Recommendation with Decomposed Adversarial Learning. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.4462-4469.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie. 2021. Fairness-aware News Recommendation with Decomposed Adversarial Learning. "Proceedings of the AAAI Conference on Artificial Intelligence". 4462-4469.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie. (2021) "Fairness-aware News Recommendation with Decomposed Adversarial Learning", Proceedings of the AAAI Conference on Artificial Intelligence, p.4462-4469

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie, "Fairness-aware News Recommendation with Decomposed Adversarial Learning", AAAI, p.4462-4469, 2021.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie. "Fairness-aware News Recommendation with Decomposed Adversarial Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.4462-4469.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie. "Fairness-aware News Recommendation with Decomposed Adversarial Learning". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 4462-4469.

Chuhan Wu||Fangzhao Wu||Xiting Wang||Yongfeng Huang||Xing Xie. Fairness-aware News Recommendation with Decomposed Adversarial Learning. AAAI[Internet]. 2021[cited 2023]; 4462-4469.


ISSN: 2374-3468


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