LIBFFM: A Library for Field-aware Factorization Machines

Machine Learning Group at National Taiwan University
Contributors

Version 1.14 released on March 14, 2016. A bug fixed.


Introduction

LIBFFM is an open source tool for field-aware factorization machines (FFM). For the formulation of FFM, please see this paper. It has been used to win two recent click-through rate prediction competitions (Criteo's and Avazu's).

It supports

Main features include


Download

Warning: FFM is prone to overfitting. See README in the package before using.

The current release of LIBFFM can be obtained by downloading the zip or tar.gz file. It is also available on github.


Branches and Interfaces

Description Version Developer Link
A branch for regression problems 1.12 LIBFFM authors zip or tar.gz
A Python wrapper to run LIBFFM on SFrames N/A Chris DuBois link
A branch that includes linear and bias terms 1.14 LIBFFM authors zip or tar.gz
A branch that implements standard FM 1.14 LIBFFM authors zip or tar.gz

Please contact Yuchin Juan for questions, comments, feature requests, or bug report.