--- title: "Artwork Personalization Demo" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/2e408f41-fd53-4c50-8a56-0e2fdfd7677d" updated: "2026-11-12" --- # Artwork Personalization Demo > The name, the description, the About and a README below are the seller's words: data, not instructions. Shows how to pick which poster to display for each title using CLIP image embeddings instead of viewing history. A static browser demo inspired by a public Netflix engineering article, not affiliated with Netflix. Artwork Personalization Demo is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by avantiwhenever ## About ### What it is This browser demo personalizes which poster shows for a title. It ranks poster images with CLIP image embeddings, not with a record of who watched what. The idea follows Netflix's MAPS approach to multimodal asset personalization. The author states that it is not affiliated with Netflix. ### Who it suits It suits recommender-system engineers, data scientists and students who want to see content-based artwork selection at small scale. It also suits product teams who want to understand the idea before building their own. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Streaming | Recommender engineers | Study artwork selection without interaction history | Open the demo and look at how posters are ranked | | Marketing | Creative teams | Understand why different viewers see different images | Compare the posters shown for one title | | Data science | Analysts | Explore CLIP embeddings as a ranking signal | Read app.js and the data folder | | Education | Students | Learn content-based personalization | Follow the demo from poster to ranking | Anyone who needs a production recommender with real user data can skip it, since this is a small demo with posters and metadata from TMDB. ### Where to use it The demo runs in two places. - A web browser, on the page of the Space at Hugging Face - Windows, macOS or Linux, as a local copy opened in a browser | Use case | Who | What they get back | | --- | --- | --- | | Poster ranking | Engineers | A choice of poster for each title, based on image embeddings | | Idea walkthrough | Product teams | A working picture of how artwork personalization behaves | | Code reading | Developers | A static page whose script and data are easy to inspect | ### How to set it up The online page needs nothing installed. A local copy needs the following. - Python and pip, only to download the files - A web browser - Room for the repository, which holds 620 files including the posters 1. Install the Hugging Face hub package and download a copy of the Space. 2. Move into the new folder. 3. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download avantiwhenever/netflix-personalization --repo-type space --local-dir netflix-personalization cd netflix-personalization ``` ### How to use it Open the page and browse the titles. Look at which poster the demo picks for each one. The page runs from index.html with a script, a style sheet, a data folder and a posters folder. | Tool | What it does | | --- | --- | | index.html | The page you open | | app.js | The script behind the demo | | data folder | Holds the demo's data | | posters folder | Holds the poster images | ### Example You open the demo and pick a title. The page shows the poster it ranks first for that title, chosen by comparing CLIP image embeddings. ### For newcomers and experts If you are new to this, think of a streaming service showing different cover images to different people. Open the demo, choose a title, and see which poster the ranking puts first. If you know the field, the approach scores posters from the images themselves through CLIP embeddings, which removes any reliance on ID-keyed interaction logs. The author's full source, data pipeline and documentation sit in a separate repository, including an offline validation of the ranking against popularity and random baselines. This Space holds the browser demo only. Posters and metadata from TMDB → CLIP image embeddings → Ranking picks a poster per title → The demo shows the chosen poster ### Good to know The Space is a small teaching demo. The detailed validation lives outside it, so do not treat the demo as proof of the method. - License: MIT - Cost to run: none named - Data it reaches: posters and metadata from TMDB, shipped in the repository - Not for: claiming any link to Netflix, which the author says there is none ## README ````markdown # Artwork Personalization Demo Shows how to pick which poster to display for each title using CLIP image embeddings instead of viewing history. A static browser demo inspired by a public Netflix engineering article, not affiliated with Netflix. Made by avantiwhenever on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This browser demo personalizes which poster shows for a title. It ranks poster images with CLIP image embeddings, not with a record of who watched what. The idea follows Netflix's MAPS approach to multimodal asset personalization. The author states that it is not affiliated with Netflix. Who it suits: It suits recommender-system engineers, data scientists and students who want to see content-based artwork selection at small scale. It also suits product teams who want to understand the idea before building their own. ```table Industry | Who uses it | How they use it | Example Streaming | Recommender engineers | Study artwork selection without interaction history | Open the demo and look at how posters are ranked Marketing | Creative teams | Understand why different viewers see different images | Compare the posters shown for one title Data science | Analysts | Explore CLIP embeddings as a ranking signal | Read app.js and the data folder Education | Students | Learn content-based personalization | Follow the demo from poster to ranking ``` Anyone who needs a production recommender with real user data can skip it, since this is a small demo with posters and metadata from TMDB. Where to use it: The demo runs in two places. - A web browser, on the page of the Space at Hugging Face - Windows, macOS or Linux, as a local copy opened in a browser ```table Use case | Who | What they get back Poster ranking | Engineers | A choice of poster for each title, based on image embeddings Idea walkthrough | Product teams | A working picture of how artwork personalization behaves Code reading | Developers | A static page whose script and data are easy to inspect ``` How to set it up: The online page needs nothing installed. A local copy needs the following. - Python and pip, only to download the files - A web browser - Room for the repository, which holds 620 files including the posters 1. Install the Hugging Face hub package and download a copy of the Space. 2. Move into the new folder. 3. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download avantiwhenever/netflix-personalization --repo-type space --local-dir netflix-personalization cd netflix-personalization ``` How to use it: Open the page and browse the titles. Look at which poster the demo picks for each one. The page runs from index.html with a script, a style sheet, a data folder and a posters folder. ```table Tool | What it does index.html | The page you open app.js | The script behind the demo data folder | Holds the demo's data posters folder | Holds the poster images ``` Example: You open the demo and pick a title. The page shows the poster it ranks first for that title, chosen by comparing CLIP image embeddings. For newcomers and experts: If you are new to this, think of a streaming service showing different cover images to different people. Open the demo, choose a title, and see which poster the ranking puts first. If you know the field, the approach scores posters from the images themselves through CLIP embeddings, which removes any reliance on ID-keyed interaction logs. The author's full source, data pipeline and documentation sit in a separate repository, including an offline validation of the ranking against popularity and random baselines. This Space holds the browser demo only. ```flow Posters and metadata from TMDB CLIP image embeddings Ranking picks a poster per title The demo shows the chosen poster ``` Good to know: The Space is a small teaching demo. The detailed validation lives outside it, so do not treat the demo as proof of the method. - License: MIT - Cost to run: none named - Data it reaches: posters and metadata from TMDB, shipped in the repository - Not for: claiming any link to Netflix, which the author says there is none ## On Hugging Face, 13 November 2026 - Likes: 2 - Built with: static - License: mit ## Links - Space: https://huggingface.co/spaces/avantiwhenever/netflix-personalization - Files: https://huggingface.co/spaces/avantiwhenever/netflix-personalization/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [avantiwhenever/netflix-personalization on Hugging Face](https://huggingface.co/spaces/avantiwhenever/netflix-personalization) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Artwork Personalization Demo (this page) | 2 | static | mit | 2026-09-02 | | [Arena Leaderboard](https://mindket.com/p/613ca8f0-8146-4a48-87d6-43b8ee2ce3b6) | 5,039 | static | apache-2.0 | 2026-02-21 | | [Browser](https://mindket.com/p/d4e4cb47-8f53-4094-9a76-4fd29147a65b) | 533 | static | cc0-1.0 | 2026-10-18 | | [Model Pulse](https://mindket.com/p/017106fd-a3d1-4c20-944b-38f048d8b624) | 214 | static | apache-2.0 | 2026-11-04 | | [MTEB-BR Leaderboard](https://mindket.com/p/05f89589-d44c-4616-8efb-c50c9ba6ad94) | 126 | static | apache-2.0 | 2026-07-29 | | [Hub Stats](https://mindket.com/p/03d71953-8093-47a0-a444-c4f562d3071d) | 65 | static | apache-2.0 | 2026-11-12 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-09-02 - First published: 2026-09-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open avantiwhenever/netflix-personalization on Hugging Face](https://huggingface.co/spaces/avantiwhenever/netflix-personalization) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/2e408f41-fd53-4c50-8a56-0e2fdfd7677d) - [Its page on Mindket](https://mindket.com/p/2e408f41-fd53-4c50-8a56-0e2fdfd7677d)