fastFM: A Library for Factorization Machines - implementation -

fastFM: A Library for Factorization Machines by Immanuel Bayer

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.

from the introduction to the lirary:

(i) Easy interfacing for dynamic and interactive languages such as R, Python and Matlab.
(ii) A Python interface that allows interactive work. (iii) A publicly available testsuite that
strongly simpli es modi cations or adding of new features. (iv) Code is released under the
BSD-license which allows the integration in (almost) any open source project.

 The GitHub repository is here: https://github.com/ibayer/fastFM

Related:Thierry Silbermann presented libFM & Factorization Machines at the Paris Machine Learning #6 Season 2. His presentaton can be from 1:24:45 to 2:10:42 minutes into the video.



Thierry Silbermann, University of Konstanz, libFM & Factorization Machines

  Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.