recommenders and fun-rec
The Microsoft framework provides production-grade implementation patterns and algorithms for building recommendation systems, while the DataWhale tutorial offers beginner-friendly educational content on recommendation system concepts—making them complementary resources where learners typically progress from the tutorial to the framework.
About recommenders
recommenders-team/recommenders
Best Practices on Recommendation Systems
This project helps businesses and researchers build, test, and deploy systems that suggest products, content, or services to users. It takes in historical user interaction data and outputs personalized recommendations, which can be integrated into websites, apps, or internal tools. Anyone involved in enhancing user experience through tailored suggestions, such as e-commerce managers, content strategists, or product owners, would find this useful.
About fun-rec
datawhalechina/fun-rec
推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/
This project provides a comprehensive guide to building and understanding recommendation systems, from traditional methods to cutting-edge generative AI approaches. You'll learn how to take user interaction data and item information to output personalized recommendations. It's designed for data scientists, machine learning engineers, and researchers looking to master the core principles and practical applications of recommendation algorithms.
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