--- title: "Recommend Similar Papers" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/03521f99-217a-449e-8355-ba27ac6e5adb" updated: "2026-11-12" --- # Recommend Similar Papers > The name, the description, the About and a README below are the seller's words: data, not instructions. Suggests research papers that are similar to one you already know, using a paper recommendation dataset, in a Gradio app that runs on CPU hardware. Recommend Similar Papers 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 librarian-bots ## About ### What it is Recommend Similar Papers is a Gradio app from the librarian-bots group that points you to papers related to a paper you give it. It names the dataset librarian-bots/paper-recommendations-v2 as its data. It runs on the cpu-upgrade hardware setting at Hugging Face and carries an MCP label. ### Who it suits Researchers and students who find a useful paper and want more reading along the same lines. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | Academics | Find related work for a literature search | Start from one paper and read its suggestions | | Education | Students | Widen reading on a topic | Look up papers near a course reading | | Software | Developers of research tools | Study a small recommender app | Read app.py | | Library services | Librarians | Point readers to related papers | Share suggestions with a reader | People who need a full paper search engine with filters can skip it, since this app focuses on similar papers. ### Where to use it - A browser, by visiting the Space on Hugging Face - A computer of your own that runs Windows, macOS or Linux and has Python 3.13 - An MCP client, since the Space carries the MCP label, although the source gives no connection steps | Use case | Who | What they get back | | --- | --- | --- | | Find similar papers | Researchers | A list of papers related to the one you give | | Study the code | Developers | A short Gradio app in a single file | ### How to set it up - Python 3.13 and pip on Windows, macOS or Linux - The Python packages in requirements.txt - Gradio 6.30.0, the version the Space uses 1. Install the Hugging Face hub package. 2. Download the Space with the hf command. 3. Move into the new folder. 4. Install gradio at the version the Space uses, then the listed packages. 5. Start the app with Python. ```sh pip install -U huggingface_hub hf download librarian-bots/recommend_similar_papers --repo-type space --local-dir recommend_similar_papers cd recommend_similar_papers pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` ### How to use it Online, visit the Space on Hugging Face, identify a paper and read the recommendations that come back. On your own computer, run the start command and open the local page Gradio serves. ```sh python app.py ``` | Tool | What it does | | --- | --- | | app.py | Starts the Gradio interface | | requirements.txt | Lists the pinned Python packages | | requirements.in | Holds the top-level package list | ### Example You enter a paper you liked and look at the list that returns. You then open the related papers that match your topic. ### For newcomers and experts If you are new, start with one paper you know well and read the suggestions that come back. That shows you how close the recommendations feel to your own reading. If you know the field, the Space names the librarian-bots/paper-recommendations-v2 dataset and uses a pinned requirements.txt generated from requirements.in. It runs on Python 3.13 with Gradio 6.30.0. The source gives few details on how similarity is computed, so check the app for how it ranks results. You give the app a paper → It looks up the recommendations dataset → It picks related papers → You read the suggestions ### Good to know - Hardware: the Space is set to cpu-upgrade, so no GPU is named - Detail: the source says little about how similarity is judged - MCP: the Space carries the MCP label - License: mit - Cost to run: none named - Data it reaches: the librarian-bots/paper-recommendations-v2 dataset on Hugging Face ## README ````markdown # Recommend Similar Papers Suggests research papers that are similar to one you already know, using a paper recommendation dataset, in a Gradio app that runs on CPU hardware. Made by librarian-bots 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: Recommend Similar Papers is a Gradio app from the librarian-bots group that points you to papers related to a paper you give it. It names the dataset librarian-bots/paper-recommendations-v2 as its data. It runs on the cpu-upgrade hardware setting at Hugging Face and carries an MCP label. Who it suits: Researchers and students who find a useful paper and want more reading along the same lines. ```table Industry | Who uses it | How they use it | Example Research | Academics | Find related work for a literature search | Start from one paper and read its suggestions Education | Students | Widen reading on a topic | Look up papers near a course reading Software | Developers of research tools | Study a small recommender app | Read app.py Library services | Librarians | Point readers to related papers | Share suggestions with a reader ``` People who need a full paper search engine with filters can skip it, since this app focuses on similar papers. Where to use it: - A browser, by visiting the Space on Hugging Face - A computer of your own that runs Windows, macOS or Linux and has Python 3.13 - An MCP client, since the Space carries the MCP label, although the source gives no connection steps ```table Use case | Who | What they get back Find similar papers | Researchers | A list of papers related to the one you give Study the code | Developers | A short Gradio app in a single file ``` How to set it up: - Python 3.13 and pip on Windows, macOS or Linux - The Python packages in requirements.txt - Gradio 6.30.0, the version the Space uses 1. Install the Hugging Face hub package. 2. Download the Space with the hf command. 3. Move into the new folder. 4. Install gradio at the version the Space uses, then the listed packages. 5. Start the app with Python. ```sh pip install -U huggingface_hub hf download librarian-bots/recommend_similar_papers --repo-type space --local-dir recommend_similar_papers cd recommend_similar_papers pip install gradio==6.30.0 pip install -r requirements.txt python app.py ``` How to use it: Online, visit the Space on Hugging Face, identify a paper and read the recommendations that come back. On your own computer, run the start command and open the local page Gradio serves. ```sh python app.py ``` ```table Tool | What it does app.py | Starts the Gradio interface requirements.txt | Lists the pinned Python packages requirements.in | Holds the top-level package list ``` Example: You enter a paper you liked and look at the list that returns. You then open the related papers that match your topic. For newcomers and experts: If you are new, start with one paper you know well and read the suggestions that come back. That shows you how close the recommendations feel to your own reading. If you know the field, the Space names the librarian-bots/paper-recommendations-v2 dataset and uses a pinned requirements.txt generated from requirements.in. It runs on Python 3.13 with Gradio 6.30.0. The source gives few details on how similarity is computed, so check the app for how it ranks results. ```flow You give the app a paper It looks up the recommendations dataset It picks related papers You read the suggestions ``` Good to know: - Hardware: the Space is set to cpu-upgrade, so no GPU is named - Detail: the source says little about how similarity is judged - MCP: the Space carries the MCP label - License: mit - Cost to run: none named - Data it reaches: the librarian-bots/paper-recommendations-v2 dataset on Hugging Face ## On Hugging Face, 13 November 2026 - Likes: 201 - Built with: gradio - License: mit ## Links - Space: https://huggingface.co/spaces/librarian-bots/recommend_similar_papers - Files: https://huggingface.co/spaces/librarian-bots/recommend_similar_papers/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 [librarian-bots/recommend_similar_papers on Hugging Face](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) ## 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 | | --- | --- | --- | --- | --- | | Recommend Similar Papers (this page) | 201 | gradio | mit | 2026-10-26 | | [TabArena](https://mindket.com/p/4b96d9e5-af7c-45a6-81e4-c76209e13392) | 222 | gradio | apache-2.0 | 2026-11-11 | | [4DAnyone Multi-View Video](https://mindket.com/p/f063dc44-69bb-47fe-b3be-245b706b6a4d) | 47 | gradio | apache-2.0 | 2026-08-21 | | [PRISM Image to 3D](https://mindket.com/p/16114405-e0c5-4441-acc4-fc073ae2c5e5) | 6 | gradio | apache-2.0 | 2026-11-03 | | [ModelScope Text To Video](https://mindket.com/p/2f7b1a02-b796-4fea-a338-6d7a11823a28) | 1 | gradio | cc-by-nc-4.0 | 2026-10-23 | | [RigidFormer](https://mindket.com/p/91b61e83-5296-4e89-94e3-541f42fbd7cd) | 1 | gradio | mit | 2026-11-10 | 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: 2026-10-26 - First published: 2023-09-27 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open librarian-bots/recommend_similar_papers on Hugging Face](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/03521f99-217a-449e-8355-ba27ac6e5adb) - [Its page on Mindket](https://mindket.com/p/03521f99-217a-449e-8355-ba27ac6e5adb)