LIBFFM: A Library for Field-aware Factorization Machines

Machine Learning Group at National Taiwan University

Contributors

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 the top-3 in recent click-through rate prediction competitions (Criteo, Avazu, Outbrain, and RecSys 2015).

It supports

l2-regularized logistic loss

Main features include

using SSE instructions to accelerate vector operations

on-disk learning that can handle data larger than the memory capacity

Download

Please download LIBFFM at github.

Branches and Interfaces