This repository collects decision tree research papers with implementations.
Open source
24 alternativesMicrosoft Recommenders
Microsoft Recommenders collects best practices for recommendation systems.
About Microsoft Recommenders
Microsoft Recommenders is an open source repository of examples and best practices for building recommendation systems. The examples are provided as Jupyter notebooks and cover five tasks: preparing data, building models, evaluating them with offline metrics, tuning hyperparameters, and operationalizing models in production on Azure. The project is part of the Linux Foundation of AI and Data.
The repository includes utilities for loading datasets in the formats different algorithms expect, evaluating model outputs, and splitting training and test data. It also includes implementations of several classical and deep learning algorithms, intended for self-study and for customization in other applications. The quick install example targets Linux and WSL. Setup on other platforms and configurations, such as GPU, Spark, and experimental features, is described in a separate Setup Guide.
Key features
- Jupyter notebook examples covering data preparation, modeling, evaluation, tuning, and deployment
- Classical and deep learning recommendation algorithms, including ALS and xDeepFM
- Utilities for loading datasets, evaluating model outputs, and splitting training and test data
- Offline evaluation metrics for recommendation algorithms
- Hyperparameter tuning and optimization examples for recommendation models
- Optional installation extras for GPU, Spark, development, and experimental models
Best for: Researchers, developers, and students who want working examples and reusable code for prototyping and testing recommendation systems.
Good for
- Open source, with a LICENSE file in the repository
- Covers the workflow from data preparation through production deployment
- Includes both classical and deep learning algorithms for study and customization
Watch out for
- Experimental models are not thoroughly tested and may require additional installation steps
- The core package runs the CPU notebooks; GPU and Spark models require the corresponding extras
- The production deployment task is described for Azure
Microsoft Recommenders at a glance
- Category
- AI & Machine Learning › AI Search & Research
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Microsoft Recommenders FAQ
Which platforms does the quick install guide cover?
The quick install example is written for Linux and WSL. Setup for other platforms, such as Windows and macOS, is covered in the Setup Guide.
Can the core package run GPU models?
The core package can run all the CPU notebooks. GPU models require the gpu extra.
Are the experimental models production-ready?
The source states that experimental models are not thoroughly tested and may require additional steps in installation.
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