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The paper introduces QIN, a query‑driven user interest network that combines a Relevance Search Unit and a Fused Attention Unit to effectively leverage full‑history user behavior for personalized search, demonstrating significant performance gains in offline benchmarks and online A/B tests.
This article explains how modern video platforms combine traditional keyword retrieval with deep‑learning‑based vector retrieval, detailing model architectures, attention mechanisms, personalization features, offline experiments, and online A/B results that show significant improvements in recall, relevance, and user experience.
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