• 23'AAAI | Fair Representation Learning for Recommendation A Mutual Information Perspective | [Group Fairness + User Fairness]
• TOIS'22 | A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation | [Group Fairness + User Fairness + Producer Fairness] | 知乎
• WWW'21 | Debiasing Career Recommendations with Neural Fair Collaborative Filtering | [Group Fairness + User Fairness] | 知乎
• WSDM'21 | Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems | [Group Fairness + User Fairness] | 知乎
• WSDM'20 | Addressing Marketing Bias in Product Recommendations | [Group Fairness + User Fairness + Item Fairness] | 知乎
• KDD'19 | Fairness in Recommendation Ranking through Pairwise Comparisons | [Group Fairness + Item Fairness] | 知乎
• CIKM'18 | Fairness-Aware Tensor-Based Recommendation | [Group Fairness + User Fairness] | 知乎
• FAT'18 | Recommendation Independence | [Group Fairness + User Fairness + Item Fairness]
• FAT'18 | Balanced Neighborhoods for Multi-sided Fairness in Recommendation | [Group Fairness + User Fairness + Item Fairness]
• NIPS'17 | Beyond Parity: Fairness Objectives for Collaborative Filtering | [Group Fairness + User Fairness] | 知乎

Others

• EMNLP'22 | MABEL Attenuating Gender Bias using Textual Entailment Data | [Group Fairness + User Fairness] | 知乎
• KDD'21 | Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning | [Group Fairness + User Fairness] | 知乎