dawenl/cofactor
CoFactor: Regularizing Matrix Factorization with Item Co-occurrence
This project helps improve personalized recommendations by incorporating how frequently items are consumed together. It takes user interaction data, like listening history or movie ratings, and outputs more accurate recommendations for individual users. It's for data scientists and machine learning engineers working on recommendation systems.
166 stars. No commits in the last 6 months.
Use this if you need to build or enhance a recommendation engine, especially when user-item interaction data is sparse, to provide more relevant suggestions.
Not ideal if your primary goal is real-time recommendation updates, as this is a batch-oriented matrix factorization approach.
Stars
166
Forks
62
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Aug 22, 2017
Commits (30d)
0
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