Neural Factorization Machines

This is our implementation for the paper:

Xiangnan He and Tat-Seng Chua (2017). Neural Factorization Machines for Sparse Predictive Analytics. In Proceedings of SIGIR '17, Shinjuku, Tokyo, Japan, August 07-11, 2017.

We have additionally released our TensorFlow implementation of Factorization Machines under our proposed neural network framework.

Please cite our SIGIR'17 paper if you use our codes. Thanks!

Author: Dr. Xiangnan He (http://www.comp.nus.edu.sg/~xiangnan/)

Example to run the codes.

python NeuralFM.py --dataset frappe --hidden_factor 64 --layers [64] --keep_prob [0.8,0.5] --loss_type square_loss --activation relu --pretrain 0 --optimizer AdagradOptimizer --lr 0.05 --batch_norm 1 --verbose 1 --early_stop 1 --epoch 200

The instruction of commands has been clearly stated in the codes (see the parse_args function).

The current implementation supports two tasks: regression and binary classification. The regression task optimizes RMSE, and the binary classification task optimizes Log Loss.

Dataset

We use the same input format as the LibFM toolkit (http://www.libfm.org/).

Split the data to train/test/validation files to run the codes directly (examples see data/frappe/).

Last Update Date: May 11, 2017