# MAPS: Netflix's Multimodal Asset Personalization at Scale

DevFeed: [MAPS: Netflix's Multimodal Asset Personalization at Scale](<https://devfeed.tech/articles/maps-netflix-s-multimodal-asset-personalization-at-scale-141.md>)

Original publisher: [Read original article](<https://netflixtechblog.com/maps-netflixs-multimodal-asset-personalization-at-scale-32f96320785e?source=rss----2615bd06b42e---4>)

Author: Netflix Technology Blog

Published: 2026-08-28T16:01:02Z

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>), [personalization](<https://devfeed.tech/topics/personalization.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [data](<https://devfeed.tech/topics/data.md>), [A/B Testing](<https://devfeed.tech/topics/a-b-testing.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [netflix](<https://devfeed.tech/tags/netflix.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [production](<https://devfeed.tech/tags/production.md>), [testing](<https://devfeed.tech/tags/testing.md>)

## AI overview

Netflix describes MAPS, a multimodal asset-personalization system that uses embeddings from artwork and video previews to address cold-start problems and personalize recommendations soon after a title launches.

## Source excerpt

By Emma Yanyang Kong, Aditya Deshpande, Asad Abbasi, Bowei Yan, David Fagnan, Ashish Rastogi, Dhaval Patel, Ray Zhang Introduction The Netflix experience is a journey of discovery. Every visual cue, from the artwork on a title to the video previews that autoplay while you browse, is there to connect you with a story you will love. We call these visual cues assets, and choosing the right one for each member is a personalization problem of its own. But which image or video preview of Squid Game should we show you? And what do we do right after a title launches, when there's far too little interaction data to know which asset we should recommend to each member? For years, our models answered the first question well and the second poorly. They learned which assets members interacted with, but treated every asset as an opaque ID, blind to what was actually in the artwork or video preview. Right after a title launched, its assets had no history, so we dialed up exploration on its assets to gather interaction data, and otherwise fell back to popularity heuristics that ignore your taste. Only once enough interactions had piled up could personalization take over. This is the classic cold-start problem. This post shares how multimodal embeddings let our models see and hear the assets they recommend, so personalization can kick in far sooner, close to a title's launch. Because a new asset arrives with its embedding the model already understands, that embedding carries member taste signals from related assets immediately. Consequently, the model needs far less interaction history before it can personalize. We cover three production systems, artwork personalization, query-aware artwork ranking, and video preview personalization, plus a cheap trick for choosing new embeddings before committing to full end-to-end integration and A/B testing. Artwork Personalization A single image is often a member's first touchpoint with a title, so we create a diverse set of artworks for each tit