A repository providing examples, reusable Python utilities, and workflow guidance for building, evaluating, and operationalizing classical and deep learning recommendation systems. It supports researchers and developers through stages from data preparation to model deployment.
Project overview
It covers five stages of recommendation model development—Prepare Data, Model, Evaluate, Model Select and Optimize, and Operationalize—along with benchmarking tools for collaborative filtering.
Project type
Workflow · Model Development · Model Runtime · Data Processing · Evaluation & Observability · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Researchers, developers, and data teams who build, evaluate, and tune custom recommendation models using Python.
Key capabilities
Includes examples and implementations of classical and deep learning recommendation algorithms.
Provides utilities for preparing and loading data required by recommendation algorithms.
Contains tools for evaluating algorithms using offline metrics.
Offers capabilities for tuning and optimizing hyperparameters for recommendation models.
Provides a benchmark notebook to evaluate and compare different collaborative filtering algorithms.
Provides examples for operationalizing models. This capability targets a production environment using the optional external Azure service.
Limitations and risks
Experimental models are not thoroughly tested and may require additional steps during installation.
Operationalizing models in a production environment targets Azure, which is an optional external service.
Getting started
Setup difficulty is rated medium and requires using environment management like uv, setting up virtual environments, installing dependencies, and running Jupyter notebooks.
To begin, install uv, create a new virtual environment, install the core recommenders package, create a Jupyter kernel, clone the repository, and run the notebook.
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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…