benfred/implicit
Fast Python Collaborative Filtering for Implicit Feedback Datasets
This helps e-commerce managers, content curators, or data scientists build effective recommendation systems. It takes historical user interaction data (like purchases, clicks, or views) and outputs personalized recommendations for individual users or similar items to what they're already engaging with. This is ideal for anyone needing to suggest products, articles, or other items to users based on past 'implicit' behaviors.
3,771 stars and 403,190 monthly downloads. Used by 1 other package. No commits in the last 6 months. Available on PyPI.
Use this if you need to quickly generate item recommendations or find similar items based on user behavior data, especially when you have a large dataset and need efficient processing.
Not ideal if your recommendation needs rely on explicit ratings (e.g., 1-5 stars) rather than implicit actions, or if you prefer a system that explains 'why' a recommendation was made.
Stars
3,771
Forks
627
Language
Python
License
MIT
Category
Last pushed
Jul 11, 2024
Monthly downloads
403,190
Commits (30d)
0
Dependencies
4
Reverse dependents
1
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