Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Best Practices on Recommendation Systems
| Date | Stars |
|---|---|
| 2026-07-24 | 21846 |
| 2026-07-25 | 21848 |
| 2026-07-28 | 21848 |
| 2026-07-30 | 21848 |
| 2026-08-06 | 21848 |
Today
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growth rate 0.00%/day
<!-- Copyright (c) Recommenders contributors. Licensed under the MIT License. --> <img src="https://raw.githubusercontent.com/recommenders-team/artwork/main/color/recommenders_color.svg" width="800"> [](https://github.com/recommenders-team/recommenders/actions/workflows/pages/pages-build-deployment) [](https://github.com/recommenders-team/recommenders/blob/main/LICENSE) [](https://github.com/psf/black) [](https://pypi.org/project/recommenders) [](https://pypi.org/project/recommenders) [<img align="left" width="300" src="https://raw.githubusercontent.com/recommenders-team/artwork/main/mix/join_recommenders_slack.svg">](https://join.slack.com/t/lfaifoundation/shared_invite/zt-2iyl7zyya-g5rOO5K518CBoevyi28W6w) <br> ## What's New (April, 2025) We reached 20,000 stars!! We are happy to announce that we have reached 20,000 stars on GitHub! Thank you for your support and contributions to the Recommenders project. We are excited to continue building and improving this project with your help. Check out the release [Recommenders 1.2.1](https://github.com/recommenders-team/recommenders/releases/tag/1.2.1)! We fixed a lot of bugs due to dependencies, improved security, reviewed the notebooks and the libraries. ## Introduction Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems. Recommenders is a project under the [Linux Foundation of AI and Data](https://lfaidata.foundation/projects/). This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks: - [Prepare Data](examples/01_prepare_data): Preparing and loading data for each recommendation algorithm. - [Model](examples/00_quick_start): Building models using various classical and deep learning recommendation algorithms such as Alternating Least Squares ([ALS](https://spark.apache.org/docs/latest/api/python/_modules/pyspark/ml/recommendation.html#ALS)) or eXtreme Deep Factorization Machines ([xDeepFM](https://arxiv.org/abs/1803.05170)). - [Evaluate](examples/03_evaluate): Evaluating algorithms with offline metrics. - [Model Select and Optimize](examples/04_model_select_and_optimize): Tuning and optimizing hyperparameters for recommendation models. - [Operationalize](examples/05_operationalize): Operationalizing models in a production environment on Azure. Several utilities are provided in [recommenders](recommenders) to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. See the [Recommenders documentation](https://readthedocs.org/projects/microsoft-recommenders/). For a more detailed overview of the repository, please see the documents on the [wiki page](https://github.com/microsoft/recommenders/wiki/Documents-and-Presentations). For some of the practical scenarios where recommendation systems have been applied, see [scenarios](scenarios). ## Getting Started We recommend [uv](https://docs.astral.sh/uv/) for environment management (10-100x faster than conda/pip), and [VS Code](https://code.visualstudio.com/) for development. To install the recommenders package and run an example notebook on Linux/WSL: ```bash # 1. Install
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d62cd29eb1d3348f, topic:kubernetes
matched fp:d62cd29eb1d3348f, topic:deep-learning
matched fp:d62cd29eb1d3348f, topic:tutorial