recommenders-team/recommenders
Best Practices on Recommendation Systems
What it does
This project is a collection of tested, ready-to-use blueprints for building recommendation systems — the technology behind 'you might also like' features seen on platforms like Netflix, Amazon, and Spotify. It gives teams a head start by providing proven approaches for suggesting relevant content, products, or connections to users based on their behavior and preferences.
Why it matters for PMs
Building a great recommendation engine from scratch is expensive and time-consuming, so having access to a widely-adopted, community-vetted toolkit can significantly accelerate a product's path to personalization — one of the highest-impact features for driving engagement and revenue. With over 21,000 stars on GitHub and more than 100 contributors, this is a well-established resource that signals strong industry trust and could reduce the engineering cost of a core competitive differentiator.
Early stage — limited signal data
Score updated Feb 18, 2026
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