recommenders-team/recommenders

Project overview

A repository providing Python utilities, examples, and best practices for prototyping, evaluating, and operationalizing recommendation systems. It covers classical and deep learning algorithms and supports a workflow from data preparation to production deployment.

Project type

Workflow · Model Development · Model Runtime · Data Processing

Deployment

Refer to project documentation

License

MIT

Best for

• Researchers and data teams needing reusable Python utilities for building, evaluating, and tuning custom recommendation models.
• Developers working with explicit or implicit feedback datasets who need to benchmark collaborative filtering algorithms before deployment.

Key capabilities

• Examples and implementations of classical and deep learning recommendation algorithms including ALS, xDeepFM, and NCF.
• Utilities for preparing and loading data for recommendation algorithms.
• Tools for evaluating algorithms with offline metrics.
• Capabilities for tuning and optimizing hyperparameters for recommendation models.
• A benchmark notebook to evaluate and compare different collaborative filtering algorithms.
• Examples for operationalizing models in a production environment on Azure.

Limitations and risks

• Experimental models are not thoroughly tested and may require additional steps during installation.
• A GPU is required for running GPU models, though the general GPU runtime requirement is not documented. A benchmark environment required 4 CPUs, 30GB of RAM, and 1 GPU GeForce GTX 1660 Ti with 6GB of memory.

Getting started

• Difficulty is rated medium. Users should install uv, create a virtual environment, install the core package, create a Jupyter kernel, clone the repository, and run the notebook.

Evidence and sources

• GitHub project description: Best Practices on Recommendation Systems
• README: We reached 20,000 stars!!
• README: This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:
• README: The table below lists the recommendation algorithms currently available in the repository.
• README: We provide a benchmark notebook to illustrate how different algorithms could be evaluated and compared. In this notebook, the MovieLens dataset is split into training/test sets at…

Reviewed at: 2026-07-17T23:00:37Z