Recommendation Systems: Candidate Generation and Ranking

Industrial recommenders use a two-stage funnel: cheap candidate generation narrows millions of items to a few hundred, then an expensive ranker scores that shortlist. Candidate generation leans on collaborative filtering, matrix factorization, and two-tower retrieval; ranking adds a heavy feature-rich model optimized for engagement. Applied-AI interviews probe this because it is the canonical ML system design and exposes how you handle cold start, scale, and the recall-versus-precision split.