Machine Learning Toolkit
21.6k 2026-04-09
recommenders-team/recommenders
A comprehensive toolkit providing best practices and implementations for building, evaluating, and operationalizing recommendation systems.
Core Features
Data preparation and loading utilities for various algorithms.
Implementations of classic and deep learning recommendation algorithms (e.g., ALS, xDeepFM).
Tools for offline evaluation and model selection/optimization.
Guidance and examples for operationalizing models in production.
Quick Start
uv pip install recommendersDetailed Introduction
Recommenders is a 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 (via Jupyter notebooks), and utilities covering the entire lifecycle of recommendation system development, from data preparation and model building with state-of-the-art algorithms to evaluation, hyperparameter tuning, and production operationalization. Its goal is to streamline experimentation and deployment of robust recommendation solutions.