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.