Diversity & Fairness in Recommender Systems - Part 1

High NDCG doesn't mean good recommendations. A model can be 20x better than random and still create a terrible user experience by recommending only organic produce, ten times in a row. In this tutorial (Part 1), we tackle the dimensions beyond accuracy that production recommender systems must balance. You'll learn why diversity matters (filter bubbles reduce discovery and basket completion), why fairness matters (exposure inequality creates "rich-get-richer" dynamics that bury 50% of your catalog), and why post-processing layers are the industry-standard solution (add diversity re-ranking and fairness monitoring without retraining your expensive base model). We walk through Maximal Marginal Relevance (MMR), the algorithm that balances relevance scores with category diversity through a tunable lambda parameter (λ=0.5 gives 67% improvement in category diversity for just 3.7% NDCG drop). You'll see how MMR transforms monotonous recommendations into complete baskets, how the Gini coefficient quantifies exposure inequality (our baseline 0.65 means the top 5% of items capture 60% of all impressions), and why 450 items getting zero recommendations is both a business problem and an ethical one. This is Part 1 covering the frameworks and metrics. Stay tuned for Part 2 where we implement MMR re-ranking and fairness monitoring in production-ready code! RecSys 6 - Part 1