Recommender System Fairness

Engineer fair recommenders. Learn user-side fairness (different users get equally good recs), item-side fairness (creators get equitable exposure), platform fairness, popularity bias and the long tail, filter bubbles and echo chambers, and the fairness-aware re-ranking layer pattern that lets you fix things without retraining the base model.