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Core
Recommendation Systems: Candidate Generation and Ranking
Industrial recommenders run a two-stage funnel: cheap candidate generation trims millions of items to a few hundred, then an expensive ranker scores that shortlist. Candidate generation relies on collaborative filtering, matrix factorization, and two-tower retrieval; ranking layers on a heavy feature-rich model optimized for engagement. AI, ML, and GenAI engineer interviews probe this because it is the canonical ML system design and reveals how you handle cold start, scale, and the recall-versus-precision split.
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Machine Learning & Data ScienceExplain recommendation approaches: collaborative filtering vs content-based, matrix factorization, and cold start.→ML System Design (Product)Design a recommendation engine for an online-course (or e-commerce) catalog.→System Design for AI in ProductionDesign a large-scale recommendation feed (retrieval then ranking) for 100M users.→ML System Design (Product)Design a music recommendation system (Spotify-style).→System Design for AI in ProductionDesign a 'People You May Know' (friend/connection recommendation) system.→System Design for AI in ProductionExplain matrix factorization for recommendation, and how it compares to modern approaches.→
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