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arXiv · 2606.31031

GenPage: Towards End-to-End Generative Homepage Construction at Netflix

Lequn Wang·Jiangwei Pan·Linas Baltrunas
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Abstract

We present GenPage, an end-to-end generative approach to Netflix homepage construction that replaces the traditional multi-stage recommender stack with a single transformer. GenPage treats the user and request context as a prompt and autoregressively generates the entire structured, multi-row homepage as the response. We adapt the LLM training recipe: pretraining on production pages, followed by post-training via weighted binary classification (WBC) or reinforcement learning (RL). For industry-scale deployment, we introduce techniques addressing cold start, model freshness, business-rule enforcement, and serving efficiency. In online A/B tests against a mature, highly optimized production homepage recommender, GenPage delivered a substantial lift on the core user engagement metric we use for launch decisions, while reducing end-to-end serving latency by 20%. Offline, two findings stand out: enriching the prompt yields a larger improvement than scaling model capacity in our current regime, and RL post-training increases homepage diversity even though diversity is not part of the objective.

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Lequn Wang, Jiangwei Pan, Linas Baltrunas. 2026-06-30. GenPage: Towards End-to-End Generative Homepage Construction at Netflix. https://doi.org/10.1145/3773078.3831915

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