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 both Tensorflow.
Here are two datasets we used in our paper. After downloaded the datasets, you can put them in the folder datasets/:
-
DIGINETICA: http://cikm2016.cs.iupui.edu/cikm-cup
There is a small dataset sample included in the folder datasets/, which can be used to test the correctness of the code.
We have also written a blog explaining the paper.
You need to run the file datasets/preprocess.py first to preprocess the data.
For example: cd datasets; python preprocess.py --dataset=sample
usage: preprocess.py [-h] [--dataset DATASET]
optional arguments:
-h, --help show this help message and exit
--dataset DATASET dataset name: diginetica/yoochoose/sampleThen 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:
usage: main.py [-h] [--dataset DATASET] [--batchSize BATCHSIZE]
[--hiddenSize HIDDENSIZE] [--epoch EPOCH] [--lr LR]
[--lr_dc LR_DC] [--lr_dc_step LR_DC_STEP] [--l2 L2]
[--step STEP] [--patience PATIENCE] [--nonhybrid]
[--validation] [--valid_portion VALID_PORTION]
parameters arguments:
epoch_num the max epoch number
train_batch_size training batch size
valid_batch_size validation batch size
hidden_size = 16 hidden size of the NGNN
lstm_forget_bias = 0.0
max_grad_norm = 1
init_scale = 0.05
learning_rate = 0.01 # 0.001 # 0.2
decay = 0.5
decay_when = 0.002 # AUC
decay_epoch = 200
sgd_opt = 'RMSProp'
beta = 0.0001
GNN_step = 3
dropout_prob = 0
adagrad_eps = 1e-5
gpu = 0
- Python 2.7
- Tensorflow 1.5.0
Please cite our paper if you use the code:
@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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