recommenders

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## What's New (April, 2025)

We reached 20,000 stars!!

We are happy to announce that we have reached 20,000 stars on GitHub! Thank you for your support and contributions to the Recommenders project. We are excited to continue building and improving this project with your help.

Check out the release [Recommenders 1.2.1](https://github.com/recommenders-team/recommenders/releases/tag/1.2.1)!

We fixed a lot of bugs due to dependencies, improved security, reviewed the notebooks and the libraries.

## Introduction

Recommenders objective is to assist researchers, developers and enthusiasts in prototyping, experimenting with and bringing to production a range of classic and state-of-the-art recommendation systems.

Recommenders is a project under the [Linux Foundation of AI and Data](https://lfaidata.foundation/projects/). 

This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. The examples detail our learnings on five key tasks:

- [Prepare Data](examples/01_prepare_data): Preparing and loading data for each recommendation algorithm.
- [Model](examples/00_quick_start): Building models using various classical and deep learning recommendation algorithms such as Alternating Least Squares ([ALS](https://spark.apache.org/docs/latest/api/python/_modules/pyspark/ml/recommendation.html#ALS)) or eXtreme Deep Factorization Machines ([xDeepFM](https://arxiv.org/abs/1803.05170)).
- [Evaluate](examples/03_evaluate): Evaluating algorithms with offline metrics.
- [Model Select and Optimize](examples/04_model_select_and_optimize): Tuning and optimizing hyperparameters for recommendation models.
- [Operationalize](examples/05_operationalize): Operationalizing models in a production environment on Azure.

Several utilities