libfm
A software implementation of factorization machines for estimating interactions between categorical variables in large datasets.
What is libfm?
libFM is a software library implementing factorization machines, a machine learning approach for estimating interactions between categorical variables in large domains. It combines the generality of feature engineering with the superiority of factorization models, making it particularly useful for recommendation systems and predictive modeling tasks where categorical interactions are important.
Data scientists, machine learning engineers, and researchers working on recommendation systems, predictive modeling, or any application requiring estimation of interactions between categorical variables with large domains.
Developers choose libFM because it provides a complete, efficient implementation of factorization machines with multiple optimization methods (SGD, ALS, MCMC), allowing flexibility in model training while handling the computational challenges of large categorical domains that traditional methods struggle with.
Overview
Library for factorization machines
Use Cases
Best For
- Building recommendation systems with categorical user-item interactions
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