ibayer/fastFM
fastFM: A Library for Factorization Machines
This library helps data scientists and machine learning practitioners build recommendation systems and predict outcomes more accurately, especially with sparse data. It takes in structured numerical data (like user-item interactions or features) and outputs predictions for regression (continuous values), classification (categories), or ranking problems (ordering preferences). It's designed for those who work with Python and are familiar with the scikit-learn API.
1,090 stars. No commits in the last 6 months.
Use this if you need to build predictive models for recommendation, classification, or regression tasks using Factorization Machines in Python, especially when dealing with high-dimensional and sparse datasets.
Not ideal if you prefer a graphical interface, require real-time streaming predictions at extremely high throughput, or are not comfortable working within a Python development environment.
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Jul 17, 2022
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