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Recommendation Fundamentals: Retrieval, Ranking, and Evaluation Basics
Build a strong foundation in recommendation systems: candidate retrieval, ranking, exploration-exploitation, and offline/online evaluation. Designed for ML and product-system interviews.
Core Mental Model: Retrieval then Ranking
Recommendation systems at scale use multi-stage pipelines:
- retrieval for high recall from massive catalogs,
- ranking for precision on a small candidate set,
- policy/reranking for business and diversity constraints.
This decomposition is both a latency and quality requirement.