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recommenders-team/recommenders

A comprehensive toolkit providing best practices, examples, and state-of-the-art algorithms to assist in prototyping, experimenting with, and operationalizing recommendation systems.

Core Features

Data preparation and loading utilities for various algorithms.

Implementations of classical and deep learning recommendation algorithms (e.g., ALS, xDeepFM).

Tools for offline evaluation and model selection/optimization.

Guidance and examples for operationalizing recommendation models in production.

Jupyter notebooks demonstrating end-to-end recommendation workflows.

Quick Start

uv pip install recommenders

Detailed Introduction

Recommenders is an open-source project under the Linux Foundation of AI and Data, designed to empower researchers, developers, and enthusiasts in the field of recommendation systems. It offers a rich collection of best practices, examples, and utilities, primarily delivered through Jupyter notebooks. The project covers the entire lifecycle of recommendation system development, from data preparation and model building with state-of-the-art algorithms to evaluation, hyperparameter tuning, and operationalization, making it an invaluable resource for both learning and production deployment.