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/)
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.
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