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Netflix says multimodal embeddings help it choose artwork and video previews even when a new title or asset has too little interaction history for an asset-ID model to learn what members prefer. Its MAPS account describes combining content signals with personalization data, testing candidates offline and online, and deploying the resulting embeddings across Netflix surfaces. The findings and deployment claims below are Netflix’s own report, not an independent evaluation.
Why new titles and assets are difficult to personalize
Netflix’s asset-ID models learn from members’ interactions with known artwork or previews. A newly created asset has no such history, so an ID-only model has little basis for predicting how a particular member will respond. Netflix describes the resulting cold-start problem as one it historically addressed by increasing exploration or relying on popularity heuristics until more interaction data accumulated.
Content embeddings offer another signal sooner: a model can represent what an asset looks or sounds like before members have interacted with it. The practical question is not just which title to recommend, but which image or preview to show a given member—and what to show immediately after a title launches.
How Netflix personalizes artwork across five canvases
For each artwork image, Netflix uses pretrained CLIP to produce a 768-dimensional image embedding. It concatenates that vector with the artwork’s learned asset-ID embedding, then feeds the combined representation through a multilayer perceptron (MLP) to score the asset for a member. The image signal is intended to let preferences learned from related visual content inform predictions for an artwork asset that has little or no interaction history of its own.
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Netflix names five artwork canvases: billboard, vertical-box, horizontal-panel, short-panel, and landscape-panel. It says CLIP’s relative invariance to crops, resizing, and aspect ratios made it possible to pool interaction signals across those surfaces in one model rather than train five separate models. In Netflix’s account, gains were greatest on canvases with the least interaction data.
Pooling also creates an imbalance problem: canvases differ in impression volume and in the kinds of interactions they generate. Netflix says it weights training examples according to the long-term reward assigned to each interaction type. That approach is meant to keep high-volume, short-term actions from dominating while avoiding a manually selected weight for every canvas.
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What the artwork ablation found
| Version | Model design | Netflix’s reported result |
|---|---|---|
| V1 | Image embeddings in separate models for each canvas | Did not produce a statistically significant online lift in the reported ablation. |
| V2 | One pooled model using asset IDs only | Did not produce a statistically significant online lift in the reported ablation. |
| V3 | One pooled model using image embeddings | The only version Netflix says produced a statistically significant online lift; the company says it runs in production. |
Netflix says this ablation used an all-platform A/B test lasting at least four weeks. It also reports a 5.691% short-panel offline lift for V3 over the combined V1 and V2 lifts. That figure is an offline result, not the online lift; the article does not disclose the online effect size. Netflix describes changes within ±1% as not significant for the reported offline metric.
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Netflix says a redesigned TV home screen made short-panel the dominant artwork canvas, even though that surface had little historical interaction data. The company reports shipping V3 ahead of the redesign and measuring it in a one-month holdback A/B test around the change. Netflix says the model significantly improved its core discovery metric and streaming hours, but does not give the effect sizes.
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How search adds the query to artwork ranking
For search results, Netflix combines the artwork model’s member-personalization score with the cosine similarity between a CLIP text embedding of the search query and the CLIP image embedding of an artwork asset. The mixing weight, α, is tuned in online A/B tests. This lets ranking account for both a member’s general taste and the specific query, rather than treating either as the only signal.
How MediaFM represents video previews
Netflix’s earlier content-aware approach, SeqCLIP, encoded preview frames with CLIP and averaged their representations. It captured visual appearance but did not represent audio or dialogue. MediaFM, which Netflix describes as its first in-house multimodal foundation model, combines three signals for each shot: visual SeqCLIP features, a pretrained speech-and-audio embedding, and captions encoded by a large-scale text model. Netflix says MediaFM was trained at a scale of 80 million shots.
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| Video-preview representation | Signals used | Evidence and reported status |
|---|---|---|
| ID-only | Asset ID and interaction history; no content signal described. | Baseline in Netflix’s comparison. |
| SeqCLIP | Visual features from preview frames. | Outperformed ID-only in Netflix’s reported ordering, but ranked below MediaFM. |
| MediaFM | Visual features, speech/audio, and caption text. | Ranked first in Netflix’s reported comparison and became the default video-preview embedding across platforms, according to the company. |
Netflix says it compared these approaches offline with inverse propensity scoring (IPS) and in a five-week online A/B test across device platforms. The reported ordering was MediaFM, then SeqCLIP, then ID-only, with the largest gains on TV. Netflix says MediaFM significantly lifted its core streaming metric over ID-only; the article does not disclose the online lift percentage.
How Netflix screens embeddings before an A/B test
Netflix describes a two-stage evaluation workflow. For offline IPS, randomized asset selection in a dedicated exploration slice provides known serving propensities. The evaluation reweights observations by the inverse of those propensities, and Netflix requires a candidate to beat the production baseline offline before allocating it A/B-test traffic.
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To narrow the number of candidates that need full evaluation, Netflix also uses a linear probe: a low-cost model that predicts a title’s debiased popularity-winning asset from the embedding alone. Netflix labels that winner using exploration data with propensity adjustment. The probe is a screening step, not a replacement for IPS or an online test. In the reported comparison, Netflix says probe accuracy, IPS, and online results all ranked MediaFM ahead of SeqCLIP; the company now uses the probe to screen each new MediaFM checkpoint.
How shared embedding infrastructure supports deployment
Netflix describes its Embedding Store as part of its AI Platform. It stores dense embeddings for titles, games, member profiles, and multimedia assets, and serves the same embeddings to both training and online inference. This separates updates to an embedding model from updates to the personalization models that consume its output.
In Netflix’s account, teams can register, backfill, and validate a new embedding separately, then expose it through configuration without changing downstream code. That arrangement supports testing a representation independently before making it available to multiple personalization systems.
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The company’s account supports a clear explanation of its system: content embeddings can give personalization models useful information before an asset has much interaction history; pooled artwork training can share signal across sparse canvases; and MediaFM adds audio and text to a visual preview representation. Netflix reports positive experimental results and production deployment for the combined artwork model and for MediaFM.
Those are company-reported methods and results. The MAPS article does not disclose all online effect sizes, so its significance claims and relative model rankings cannot be translated into a specific expected increase for an individual member or title. Nor does the account independently establish how the system performs outside Netflix’s own products and experiments.
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