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Scaling Recommendation Systems

Architecture patterns for recommendation systems serving millions of users: candidate generation, ranking, and infrastructure.

How do you scale recommendation systems to millions of users and items?

Scaling requires approximate nearest neighbor search instead of brute-force, two-stage retrieval (candidate generation + ranking), embedding pre-computation, feature stores with millisecond latency, and infrastructure separating training from serving.

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Embeddings and Vector Search for Recommendations

How modern recommendation systems use neural embeddings and approximate nearest neighbor search for personalization at scale.

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Scaling Recommendation Systems

How do you scale recommendation systems to millions of users and items?

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