Gavince/Recommend-System
深度学习与推荐系统学习，理论结合代码更香。
This project offers a practical guide to building and understanding modern recommendation systems. It demonstrates how to take a large catalog of items and user interaction data, identify potentially interesting items (recall), narrow down choices (ranking), and present a personalized list of recommendations. E-commerce managers, content platform strategists, or anyone building user-facing product recommendations would find this useful.
162 stars. No commits in the last 6 months.
Use this if you need to implement or improve a system that suggests products, videos, articles, or other items to users based on their past behavior and preferences.
Not ideal if you are looking for a plug-and-play solution without any technical implementation or understanding of the underlying algorithms.
Stars
162
Forks
23
Language
Jupyter Notebook
License
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Category
Last pushed
Jul 30, 2022
Commits (30d)
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