# fastFM: A Library for Factorization Machines

arXiv: https://arxiv.org/abs/1505.00641 · HTML: https://mlsanity.com/paper/1505.00641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2015.

## Built on by (most cited first, 1 of 1)

- [Memory-Efficient Factorization Machines via Binarizing both Data and Model Coefficients](https://mlsanity.com/paper/2108.07421/) (2021)

## Abstract (arXiv)

Factorization Machines (FM) are only used in a narrow range of applications and are not part of the standard toolbox of machine learning models. This is a pity, because even though FMs are recognized as being very successful for recommender system type applications they are a general model to deal with sparse and high dimensional features. Our Factorization Machine implementation provides easy access to many solvers and supports regression, classification and ranking tasks. Such an implementation simplifies the use of FM's for a wide field of applications. This implementation has the potential to improve our understanding of the FM model and drive new development.