• WWW'22 | FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback | [Individual Fairness + Item Fairness] | 知乎
• KDD'22 | Comprehensive Fair Meta-learned Recommender System | [Individual Fairness + Group Fairness + User Fairness] | 知乎
• Arxiv'22 | FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation | [Individual Fairness + User Fairness]
• AAAI'21 | Fairness-aware News Recommendation with Decomposed Adversarial Learning | [Individual Fairness + User Fairness] | 知乎
• SIGIR'21 | Towards Personalized Fairness based on Causal Notion | [Individual Fairness + User Fairness] | 知乎
• WWW'21 | Learning Fair Representations for Recommendation: A Graph-based Perspective | [Individual Fairness + User Fairness] | 知乎
• WSDM'21 | Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information | [Individual Fairness + User Fairness] | 知乎
• SIGIR'20 | Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems | [Group Fairness + Item Fairness] | 知乎
• WSDM'20 | Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning | [Individual Fairness + User Fairness]
• ICML'19 | Compositional Fairness Constraints for Graph Embeddings | [Individual Fairness + User Fairness] | 知乎

Other Fields

• ICCV'19 | Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations
• EMNLP'18 | Adversarial Removal of Demographic Attributes from Text Data
• NAACL'18 | KBGAN: Adversarial Learning for Knowledge Graph Embeddings
• NIPS'17 | Controllable invariance through adversarial feature learning
• FAT'17 | Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
• ICLR'15 | Censoring Representations with an Adversary