--- title: "paper-rec" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d0ef4dd5-694a-4550-b912-b6da9531d8cf" updated: "2026-11-12" --- # paper-rec > The name, the description, the About and a README below are the seller's words: data, not instructions. A Gradio demo that recommends ten recent machine learning and AI papers from arXiv, based on titles, abstracts or keywords you give it. It suits researchers and students. paper-rec 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 bluebalam ## About ### What it is paper-rec is a Gradio demo on Hugging Face Spaces that recommends machine learning and AI papers. So many papers appear each day that choosing the next one to read is hard. This demo picks from the latest arXiv papers and tailors the list to what you liked before. ### Who it suits It suits researchers, students and engineers who follow machine learning and want a short reading list without scanning every new paper. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Academia | PhD students | Find new papers close to ones they already liked | Enter the title of a paper from their reading list | | Industry research | ML engineers | Keep up with a topic without scanning daily listings | Enter a few topic keywords | | Education | Course instructors | Gather recent papers on a subject for a class | Enter an abstract that matches the lesson | Anyone who needs papers outside machine learning and AI can skip it, since the demo is aimed at that field. ### Where to use it - A web browser, straight from the Space page on Hugging Face - Windows, macOS or Linux with Python, as a local copy | Use case | Who | What they get back | | --- | --- | --- | | Find the next paper to read | Researchers | A top-10 list of recommended articles | | Explore a topic by keywords | Students | Recent arXiv papers close to the keywords | ### How to set it up Using it online needs nothing. A local copy needs these first. - Python and pip - The huggingface\_hub package, to fetch the files - The packages in requirements.txt, plus Gradio 1. Install the Hugging Face Hub package. 2. Download the Space into a folder called paper-rec. 3. Move into that folder. 4. Install Gradio, then the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download bluebalam/paper-rec --repo-type space --local-dir paper-rec cd paper-rec pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Open the Space page on Hugging Face and enter the title or abstract of papers you liked, or both. You can also type keywords for topics you care about. The demo then gives you ten recommended articles. ### Example You enter the title of a paper you enjoyed last month. The demo answers with ten recent papers that match your taste, picked from the latest arXiv submissions. ### For newcomers and experts If you are new, start with one paper you liked and paste its title. You will get a list of ten papers to try, so you do not have to search by hand. If you know the field, the input is free text, a title, an abstract or keywords, and the output is a fixed top-10 list from recent arXiv papers. The source does not say how similarity is computed, so read app.py for the method. You enter titles, abstracts or keywords → The demo compares them with recent arXiv papers → You get ten recommended articles ### Good to know - It covers recent arXiv papers in machine learning and AI, so older or other-field papers may not appear - The method behind the ranking is in app.py, not in the README - License: mit - Cost to run: none named - Data it reaches: arXiv paper listings and the text you enter ## README ````markdown # paper-rec A Gradio demo that recommends ten recent machine learning and AI papers from arXiv, based on titles, abstracts or keywords you give it. It suits researchers and students. Made by bluebalam 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: paper-rec is a Gradio demo on Hugging Face Spaces that recommends machine learning and AI papers. So many papers appear each day that choosing the next one to read is hard. This demo picks from the latest arXiv papers and tailors the list to what you liked before. Who it suits: It suits researchers, students and engineers who follow machine learning and want a short reading list without scanning every new paper. ```table Industry | Who uses it | How they use it | Example Academia | PhD students | Find new papers close to ones they already liked | Enter the title of a paper from their reading list Industry research | ML engineers | Keep up with a topic without scanning daily listings | Enter a few topic keywords Education | Course instructors | Gather recent papers on a subject for a class | Enter an abstract that matches the lesson ``` Anyone who needs papers outside machine learning and AI can skip it, since the demo is aimed at that field. Where to use it: - A web browser, straight from the Space page on Hugging Face - Windows, macOS or Linux with Python, as a local copy ```table Use case | Who | What they get back Find the next paper to read | Researchers | A top-10 list of recommended articles Explore a topic by keywords | Students | Recent arXiv papers close to the keywords ``` How to set it up: Using it online needs nothing. A local copy needs these first. - Python and pip - The huggingface_hub package, to fetch the files - The packages in requirements.txt, plus Gradio 1. Install the Hugging Face Hub package. 2. Download the Space into a folder called paper-rec. 3. Move into that folder. 4. Install Gradio, then the packages from requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download bluebalam/paper-rec --repo-type space --local-dir paper-rec cd paper-rec pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Open the Space page on Hugging Face and enter the title or abstract of papers you liked, or both. You can also type keywords for topics you care about. The demo then gives you ten recommended articles. Example: You enter the title of a paper you enjoyed last month. The demo answers with ten recent papers that match your taste, picked from the latest arXiv submissions. For newcomers and experts: If you are new, start with one paper you liked and paste its title. You will get a list of ten papers to try, so you do not have to search by hand. If you know the field, the input is free text, a title, an abstract or keywords, and the output is a fixed top-10 list from recent arXiv papers. The source does not say how similarity is computed, so read app.py for the method. ```flow You enter titles, abstracts or keywords The demo compares them with recent arXiv papers You get ten recommended articles ``` Good to know: - It covers recent arXiv papers in machine learning and AI, so older or other-field papers may not appear - The method behind the ranking is in app.py, not in the README - License: mit - Cost to run: none named - Data it reaches: arXiv paper listings and the text you enter ## On Hugging Face, 13 November 2026 - Likes: 10 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/bluebalam/paper-rec - Files: https://huggingface.co/spaces/bluebalam/paper-rec/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, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [bluebalam/paper-rec on Hugging Face](https://huggingface.co/spaces/bluebalam/paper-rec) ## 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 | | --- | --- | --- | --- | --- | | paper-rec (this page) | 10 | gradio | mit | 2022-10-26 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## 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: 2022-10-26 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open bluebalam/paper-rec on Hugging Face](https://huggingface.co/spaces/bluebalam/paper-rec) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d0ef4dd5-694a-4550-b912-b6da9531d8cf) - [Its page on Mindket](https://mindket.com/p/d0ef4dd5-694a-4550-b912-b6da9531d8cf)