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Fast and memory efficient library for factorization machines (FM).

• Supports both ℓ1 regularized logistic regression and factorizationmachines.
• Runs on local machine and distributed clusters.
• Scales to datasets with billions examples and features.

The following commands clone and build difacto, then download a sample dataset,and train FM with 2-dimension on it.

git clone --recursive https://github.com/CNevd/DiFactocd DiFacto; make -j8./tools/download.sh gisettebuild/difacto xx.conf data_in=data/gisette_scale data_val=data/gisette_scale.t lr=.02 V_dim=2 V_lr=.001

Mu Li, Ziqi Liu, Alex Smola, and Yu-Xiang Wang.DiFacto — Distributed Factorization Machines. In WSDM, 2016