ibayer/fastFM

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1086 stars · 204 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

fastFM is a Python library for Factorization Machines with a scikit-learn compatible API, with performance-critical code written in C and wrapped with Cython. It supports regression, classification, and ranking tasks via SGD, ALS (coordinate descent), and MCMC solvers.

Use cases

• train factorization machine models for regression
• build recommender systems with matrix factorization
• binary classification with factorization machines
• learning-to-rank with BPR loss
• Bayesian inference for factorization machines with MCMC
• sparse feature interaction modeling in scikit-learn style

When to choose

• you need factorization machines with a familiar scikit-learn API
• you want multiple solvers (SGD, ALS, MCMC) for regression, classification, or ranking
• you work on Linux or macOS with Python

When to avoid

• you need Windows support
• you need actively maintained software with recent updates
• you need deep learning based recommenders or GPU acceleration

Facets

library · maturity maintenance

machine-learning data-science machine-learning python factorization-machines matrix-factorization scikit-learn-api recommender-systems cython linux macos

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For agents

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