Papers

Organize Fairness-In-Recommendation-System into three categories:

• Pre-Processing
  • Re-sampling
  • Data Augmentation
  • Re-weighing
  • Influence Function
• In-Processing
  • Adversary Learning
  • Regularization and Constrained Optimization
  • Reinforcement Learning
  • Re-weighing
  • AutoEncoder
  • Causal Inference
  • Invariant Learning
  • Others
• Post-Processing
  • Slot-wise Re-ranking
  • User-wise Re-ranking
  • Global-wise Re-ranking
• Survey

Simultaneously, these papers are also marked with some criteria:

• Group Fairness vs. Individual Fairness
• User Fairness vs. Item Fairness

Related Resources

• Fairness in AI
• Recommendation Debiasing
• Datasets