Fairness-aware News Recommendation with Decomposed Adversarial Learning

@inproceedings{Wu2021FairnessawareNR,
  title={Fairness-aware News Recommendation with Decomposed Adversarial Learning},
  author={Chuhan Wu and Fangzhao Wu and Xiting Wang and Yongfeng Huang and Xing Xie},
  booktitle={AAAI},
  year={2021}
}
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… 

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