Recommenders joins LF AI & Data as new Sandbox project

Linux Foundation (LF) AI & Data Foundation—the organization building an ecosystem to sustain open source innovation in AI and data open source projects, announced Recommenders as its latest Sandbox project.

Recommenders is an open source Github repository designed to assist researchers, developers, and enthusiasts in prototyping, experimenting with, and bringing to production a wide range of classic and state-of-the-art recommendation algorithms. By providing valuable examples such as Jupyter notebooks and establishing best practices for building recommendation systems, Recommenders aims to democratize and streamline the development of these crucial technologies. Learn more about Recommenders on their GitHub and join the Recommenders-Announce Mailing List.

“The Recommenders project exemplifies our commitment to fostering an open source community that encourages collaboration and learning in recommendation systems and machine learning and aligns perfectly with LF AI & Data’s mission to advance AI and data technologies through shared knowledge and collaborative development.”—Dr. Ibrahim Haddad, Executive Director of LF AI & Data.

Addressing the challenges within recommendation systems, Recommenders offers solutions that cater to the growing demand for scalable and enterprise-grade approaches. The project acknowledges the limited resources and fragmented solutions that hinder the efficient development of recommender systems. By cultivating a collection of modular utilities, diverse algorithms, and educational notebooks, Recommenders simplifies the creation, evaluation, and deployment of recommendation technologies.

Key highlights of Recommenders:

• Libraries: A comprehensive collection of modular functions for model creation, data manipulation, evaluation, and more, conveniently accessible through the PyPI package “recommenders”. This package offers a range of algorithms and utilities essential for building recommendation solutions.
• Examples: Offers comprehensive how-to examples for building end-to-end recommendation systems.
• Robust testing framework: With over 900 tests meticulously designed, we ensure that both the library and examples function seamlessly. This comprehensive testing framework guarantees the accuracy and reliability of our solutions, providing developers with confidence in their deployment.

Goals of Recommenders:

• Bridge the gap between research and application by making recommendation technology accessible to a broader audience.
• Empower researchers and developers to select, prototype, demonstrate efficiently, and productionize recommender systems.
• Accelerate the development and deployment of enterprise-grade recommender systems.
• Provide a systematic overview of recommendation technology from a pragmatic perspective.
• Showcase state-of-the-art academic research in recommendation algorithms.
• Share best practices, complete with example codes, for developing effective recommender systems.

“At Recommenders, our goal has always been to take recommendation technology to the masses. We are excited to partner with LF AI & Data to further this mission. This collaboration will enable us to provide developers and researchers with an even more robust platform to prototype, develop, and deploy recommendation systems, thereby accelerating innovation in this dynamic field.”—Miguel Fierro, Ph.D., Data Scientist Manager at Microsoft.

With an impressive 16,000 stars and 2,800 forks on GitHub, Recommenders stands as the leading open source repository of recommendation systems. It has also gained recognition within academia, being utilized by researchers submitting papers to the renowned RecSys conference. Prestigious platforms like YC Hacker News, O’Reilly Data Newsletter, and GitHub’s weekly trending list have acknowledged the project’s impact.

Recommendation systems are vital components of diverse industries, particularly in e-commerce. Recommenders alleviates the complexities of building such systems, offering a comprehensive library, illustrative Jupyter notebook examples, and a robust testing pipeline. The project thrives on contributions from a vibrant open source community that embraces and champions its vision.

Learn more about Recommenders on their GitHub and join the Recommenders-Announce Mailing List.

A warm welcome to Recommenders! We are excited to see the project’s continued growth and success as part of the LF AI & Data Foundation. If you are interested in hosting an open source project with us, please visit the LF AI & Data website to learn more.

Recommenders key links

• GitHub
• Mail Lists:
  • Recommenders-Announce
  • Recommenders-Technical-Discuss
  • Recommenders TSC

• Artwork

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Mark Russinovich

CTO, Microsoft Azure

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