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

DevFeed: [GenPage: Towards End-to-End Generative Homepage Construction at Netflix](<https://devfeed.tech/articles/genpage-towards-end-to-end-generative-homepage-construction-at-netflix-136.md>)

Original publisher: [Read original article](<https://netflixtechblog.com/genpage-towards-end-to-end-generative-homepage-construction-at-netflix-77146fba8a08?source=rss----2615bd06b42e---4>)

Author: Netflix Technology Blog

Published: 2026-06-29T13:01:02Z

Content type: article

Language: en

Sources: [Netflix](<https://devfeed.tech/sources/netflix.md>), [Netflix TechBlog - Medium](<https://devfeed.tech/sources/netflix-techblog-medium.md>)

Topics: [Netflix](<https://devfeed.tech/topics/netflix.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [app](<https://devfeed.tech/tags/app.md>), [diversity](<https://devfeed.tech/tags/diversity.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [feature-engineering](<https://devfeed.tech/tags/feature-engineering.md>), [generation](<https://devfeed.tech/tags/generation.md>), [generative](<https://devfeed.tech/tags/generative.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [netflix](<https://devfeed.tech/tags/netflix.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [ranking](<https://devfeed.tech/tags/ranking.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [rl](<https://devfeed.tech/tags/rl.md>)

## AI overview

This Netflix developer article introduces GenPage, a generative approach that uses a single autoregressive model to construct a personalized homepage by generating recommendation rows, entities, and layout together. It describes replacing a multi-stage recommendation pipeline with end-to-end modeling and using reinforcement learning to optimize whole-page rewards, including interactions such as diversity and the balance between rows.

## Source excerpt

Authors: Lequn Wang, Jiangwei Pan, and Linas Baltrunas Figure 1. Autoregressive homepage generation. GenPage builds a Netflix homepage one row or entity at a time, each one conditioned on what's already on the page and the user's context.Introduction The Netflix homepage is the first thing users see when they open the app and the primary way they discover content to enjoy. Almost every part of it is personalized, including which rows appear, which entities show up within those rows, and how everything is arranged on the page. Constructing that homepage is a genuinely hard problem. It is not simply producing one ranked list. The homepage is a structured, two-dimensional layout, made up of recommendation rows and the entities within them. Here, an entity can be a movie, show, game, live event, or other recommendable item. Each choice can affect the value of the others. Traditionally, it is built through a complex, multi-stage pipeline, with separate components for candidate generation and ranking at both the row and entity levels. We saw an opportunity to rethink this design. Large language models have shown that a single generative model can perform diverse tasks just by generating a response to a prompt. Inspired by this prompt-response paradigm, we trained a single generative model to build the homepage by directly answering one question: Given everything we know about this user and this request, what homepage should we generate to maximize user satisfaction? We call this approach GenPage. It treats the user history and request context as the prompt, and autoregressively generates the entire homepage as the response (Figure 1). Unlike most generative recommenders, such as TIGER, HSTU, and OneRec, which generate flat ranked lists, GenPage generates the rows, entities, and layout together. This shift is motivated by several goals: End-to-end modeling. A single transformer model that constructs the page from raw input signals can replace a complex multi-stage recommen