qcymkxyc/RecSys
项亮的《推荐系统实践》的代码实现
This project provides practical examples and code for building recommendation systems. It takes historical user interaction data with items (like movie ratings or purchases) and outputs personalized recommendations, helping businesses suggest relevant products or content to their customers. E-commerce managers, content strategists, or anyone needing to personalize user experiences would find this useful.
498 stars. No commits in the last 6 months.
Use this if you need to understand and apply core recommendation system algorithms using real-world datasets like MovieLens.
Not ideal if you are looking for a plug-and-play recommendation service rather than an educational implementation of algorithms.
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498
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137
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Jupyter Notebook
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Last pushed
Oct 30, 2020
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