Recommenders
Best Practices on Recommendation Systems
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What is Recommenders?
Recommenders is an open-source project under the Linux Foundation of AI and Data that provides examples and best practices for building recommendation systems. The project assists researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production a range of classic and state-of-the-art recommendation systems through Jupyter notebooks.
The repository covers five key tasks in the recommendation system lifecycle: preparing and loading data for each algorithm, building models using classical and deep learning algorithms (such as Alternating Least Squares and eXtreme Deep Factorization Machines), evaluating algorithms with offline metrics, tuning and optimizing hyperparameters, and operationalizing models in production environments on Azure. The recommenders library provides utilities for common tasks like loading datasets in expected formats, evaluating model outputs, and splitting training/test data.
Recommenders includes implementations of 30+ state-of-the-art algorithms including collaborative filtering methods (ALS, NCF, LightGCN, SASRec), content-based filtering algorithms (DKN, LightGBM, TF-IDF), and sequential recommendation models (GRU, NextItNet, SLi-Rec). It supports both CPU and GPU environments, PySpark for distributed computing, and includes extras for GPU models, Spark models, development tools, and experimental features. The project has over 21,000 GitHub stars and is widely used by the machine learning community.
Recommenders pricing
Pricing model: Freemium
Free and open-source under MIT license. The core recommenders package is completely free with no paid tiers. Install via pip install recommenders. Additional extras available: [gpu] for GPU models, [spark] for Spark models, [dev] for development, [all] for all extras combined, and [experimental] for models not thoroughly tested. No commercial licensing or subscription required.
Recommenders pros
- Open-source under MIT license with no cost
- 30+ state-of-the-art recommendation algorithms included
- Jupyter notebooks provide ready-to-run examples
- Covers complete ML lifecycle from data prep to production
- Supports both CPU and GPU environments
- Includes PySpark support for distributed computing
- Utilities for loading datasets in algorithm-specific formats
- Built-in evaluation metrics for offline model assessment
- Hyperparameter tuning and optimization guidance included
- Azure integration for production operationalization
- Active community with 21,000+ GitHub stars
- 107 contributors and regular updates
- Supports collaborative filtering and content-based filtering
- Includes sequential recommendation algorithms
- Comprehensive documentation and wiki available
- Multiple extras packages for GPU, Spark, and experimental features
- Benchmark notebooks for algorithm comparison
Recommenders cons
- Requires conda environment setup which adds complexity
- Requires gcc installation on Linux/WSL systems
- GPU models need additional [gpu] extras installation
- Spark models require separate [spark] extras installation
- Some algorithms marked experimental and not thoroughly tested
- Setup more complex on Windows and macOS than Linux
- Requires Jupyter kernel creation step before running notebooks
- Heavy dependency on Azure for production operationalization examples
- Some algorithms only work in specific environments (CPU-only or GPU-only)
- Learning curve for beginners unfamiliar with recommendation systems
Frequently asked questions about Recommenders
What is Recommenders?
Recommenders is a project under the Linux Foundation of AI and Data that contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. Its objective is to assist researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production classic and state-of-the-art recommendation systems.
What algorithms are available in Recommenders?
Recommenders includes 30+ algorithms such as Alternating Least Squares (ALS), Neural Collaborative Filtering (NCF), LightGCN, Deep Knowledge-Aware Network (DKN), eXtreme Deep Factorization Machines (xDeepFM), SASRec, GRU, SAR, RBM, Wide and Deep, TF-IDF, LightGBM, and many more. These cover collaborative filtering, content-based filtering, and sequential recommendation approaches.
How do I install Recommenders?
Install using conda for environment management: create a conda environment with Python 3.9, activate it, then run pip install recommenders for the core package. You'll also need to create a Jupyter kernel and clone the GitHub repository. For GPU support, use pip install recommenders[gpu]; for Spark, use pip install recommenders[spark].
What environments does Recommenders support?
Recommenders supports CPU environments for all algorithms, GPU environments for deep learning models, and PySpark for distributed computing algorithms like ALS. Different algorithms work in different environments - some are CPU-only, while others support CPU/GPU. The Setup Guide provides detailed instructions for Windows, macOS, and Linux/WSL.
What are the five key tasks covered in Recommenders?
The five key tasks are: 1) Prepare Data - preparing and loading data for each recommendation algorithm, 2) Model - building models using classical and deep learning algorithms, 3) Evaluate - evaluating algorithms with offline metrics, 4) Model Select and Optimize - tuning and optimizing hyperparameters, and 5) Operationalize - operationalizing models in production environments on Azure.
How do I run the example notebooks?
After installing and setting up the Jupyter kernel, clone the repository using git clone https://github.com/recommenders-team/recommenders.git. Open a notebook in VS Code (e.g., examples/00_quick_start/sar_movielens.ipynb), select your Jupyter kernel, and run the notebook. VS Code is recommended for development along with conda for environment management.
What utilities does the recommenders library provide?
The recommenders library provides utilities for loading datasets in the format expected by different algorithms, evaluating model outputs with offline metrics, and splitting training/test data. These utilities support common tasks across the recommendation system development lifecycle.
Can I use Recommenders for production systems?
Yes, Recommenders is designed to help bring recommendation systems to production. It includes operationalization examples for Azure production environments, best practices for production-ready systems, and utilities for evaluating model outputs. The project references papers on building production-ready recommendation systems at scale.
What extras packages are available?
Available extras include: [gpu] for running GPU models, [spark] for running Spark models, [dev] needed for development of the repo, [all] which combines gpu, spark, and dev, and [experimental] for models that are not thoroughly tested and/or may require additional installation steps.
How can I contribute to Recommenders?
The project welcomes contributions and suggestions. Contributors should review the contribution guidelines before submitting. The project adheres to a Code of Conduct to foster a welcoming community. Independent or incubating algorithms and utilities can be contributed to the contrib folder, which houses contributions that may need time to mature.