https://github.com/hexiangnan/neural_factorization_machine
TenforFlow Implementation of Neural Factorization Machine
https://github.com/hexiangnan/neural_factorization_machine
deep-learning factorization-machines neural-factorization-machines recommender-system
Last synced: 6 months ago
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TenforFlow Implementation of Neural Factorization Machine
- Host: GitHub
- URL: https://github.com/hexiangnan/neural_factorization_machine
- Owner: hexiangnan
- Created: 2017-08-02T15:07:12.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-03-01T09:05:52.000Z (over 5 years ago)
- Last Synced: 2025-03-29T16:08:35.130Z (6 months ago)
- Topics: deep-learning, factorization-machines, neural-factorization-machines, recommender-system
- Language: Python
- Homepage:
- Size: 19.6 MB
- Stars: 470
- Watchers: 18
- Forks: 185
- Open Issues: 13
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Metadata Files:
- Readme: README.md
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README
# Neural Factorization Machines
This is our implementation for the paper:
Xiangnan He and Tat-Seng Chua (2017). [Neural Factorization Machines for Sparse Predictive Analytics.](http://www.comp.nus.edu.sg/~xiangnan/papers/sigir17-nfm.pdf) 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