{"ID":5438567,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-03T01:40:09.565152011Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31031","arxiv_id":"2606.31031","title":"GenPage: Towards End-to-End Generative Homepage Construction at Netflix","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, the WBC variant of GenPage delivered a +0.24% lift on the core user engagement metric we use for launch decisions (p \u003c 0.001), 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.","short_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....","url_abs":"https://arxiv.org/abs/2606.31031","url_pdf":"https://arxiv.org/pdf/2606.31031v1","authors":"[\"Lequn Wang\",\"Jiangwei Pan\",\"Fengdi Che\",\"Linas Baltrunas\"]","published":"2026-06-30T02:00:05Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Reinforcement Learning\",\"Transformer\",\"Large Language Model\"]","has_code":false}