Two-tower embeddings
User tower + item tower. Trained on watch history. At serve time, retrieve top-K items by embedding similarity. Sub-linear.
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Two-tower embeddings
User tower + item tower. Trained on watch history. At serve time, retrieve top-K items by embedding similarity. Sub-linear.
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Candidate gen narrows
15K titles → 500 candidates via embeddings + business rules (region availability). Fast.