Ever wonder how Netflix seems to *know* your next binge-worthy show? Behind every "Recommended for You" row is a staggeringly complex AI pipeline—candidate generation, ranking, reranking, and a feature store stitching it all together. This episode breaks down how modern recommendation engines blend battle-tested techniques (like matrix factorization and gradient-boosted trees) with cutting-edge AI (embeddings, two-tower models, and even LLMs). We’ll explore why these systems use cascading stages instead of one giant model, how real-time features keep suggestions fresh, and where the next breakthroughs might come from.
