2016 FastFMALibraryforFactorizationM

• (Bayer, 2016) ⇒ Immanuel Bayer. (2016). “fastFM: A Library for Factorization Machines.” In: The Journal of Machine Learning Research, 17(1).

Subject Headings: fastFM.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+fastFM%3A+A+Library+for+Factorization+Machines
• http://dl.acm.org/citation.cfm?id=2946645.3053466&preflayout=flat#citedby

Quotes

Abstract

Factorization Machines (FM) are currently only used in a narrow range of applications and are not yet part of the standard machine learning toolbox, despite their great success in collaborative filtering and click-through rate prediction. However, Factorization Machines are a general model to deal with sparse and high dimensional features. Our Factorization Machine implementation (fastFM) provides easy access to many solvers and supports regression, classification and ranking tasks. Such an implementation simplifies the use of FM for a wide range of applications. Therefore, our implementation has the potential to improve understanding of the FM model and drive new development.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 FastFMALibraryforFactorizationM	Immanuel Bayer			fastFM: A Library for Factorization Machines