The code and dataset for our paper in the WebConf2019: Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks [arXiv version]
This is the code for the WWW-2019 Paper: Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks. We have implemented our methods in Tensorflow.
The original Polyvore dataset we used in our paper is first proposed here. After downloaded the datasets, you can put them in the folder NGNN/data/:
You can download the preprocessed data here, https://drive.google.com/open?id=1ibYEw0H9L9O9OLbxCiAlcZkt_IYuwKfd and also put them in the folder NGNN/data/.
There is a small dataset sample included in the folder NGNN/data/, which can be used to test the correctness of the code.
Then you can run the file NGNN/main_score.py to train the model.
You can change parameters according to the usage in NGNN/Config.py:
parameters arguments in `NGNN/Config.py`:
epoch_num the max epoch number
train_batch_size training batch size
valid_batch_size validation batch size
hidden_size hidden size of the NGNN
lstm_forget_bias forget bias in NGNN update
max_grad_norm the gradient clip during train
init_scale the scale of initialize parameter 0.05
learning_rate learning rate 0.01 # 0.001 # 0.2
decay the decay of 0.5
decay_when = 0.002 # AUC
decay_epoch = 200
sgd_opt train strategy can choose: 'RMSProp', 'Adam', 'Momentum', 'RMSProp', 'Adadelta'
beta the weight of regulartion
GNN_step the number of step of GNN
dropout_prob the dropout probability of our model
adagrad_eps eps
gpu = 0 the gpu id
- Python 2.7
- Tensorflow 1.5.0
Please cite our paper if you find the code useful:
@inproceedings{cui2019dressing,
title={Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks},
author={Cui, Zeyu and Li, Zekun and Wu, Shu and Zhang, Xiao-Yu and Wang, Liang},
booktitle={The World Wide Web Conference},
pages={307--317},
year={2019},
organization={ACM}
}
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