『Netflix — One Model, One Pass, Your Whole Homepage』のカバーアート

Netflix — One Model, One Pass, Your Whole Homepage

Netflix — One Model, One Pass, Your Whole Homepage

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10月19日まで。※適用条件あり
A deep dive into how Netflix replaced its traditional multi-stage homepage recommender — separate systems for candidate retrieval, ranking, and row layout — with a single transformer that generates the entire structured, multi-row homepage in one pass. We cover the business problem (stitching together multiple separately-trained models to build one coherent homepage adds engineering complexity and serving latency, while still needing to handle cold start, freshness, and business-rule placement constraints in production), the technical approach (treating the user's history and request context as a prompt and autoregressively generating the whole homepage as the response, trained with an LLM-style recipe of pretraining on historical homepages followed by post-training via either weighted binary classification or reinforcement learning), and what the reported results actually show — a substantial lift on the core engagement metric alongside a 20% cut in end-to-end serving latency, plus the surprising offline finding that enriching the prompt beat simply scaling up the model, and that reinforcement learning increased homepage diversity as a side effect even though diversity was never an explicit training objective. Source article: "GenPage: Towards End-to-End Generative Homepage Construction at Netflix" — Netflix / arXiv, accepted RecSys 2026, https://arxiv.org/abs/2606.31031 (published 2026-06).
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