fastFM: A Library for Factorization Machines
This work addresses the problem of underutilization of FM models in machine learning, particularly for researchers and practitioners dealing with sparse and high-dimensional data, though it is incremental as it focuses on implementation rather than new algorithmic advances.
The authors tackled the limited adoption of Factorization Machines (FM) by developing fastFM, a library that provides easy access to multiple solvers and supports regression, classification, and ranking tasks, aiming to simplify FM use across various applications.
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.