dmlc--dgl
WangYQ
f5bba284f8
* add eges example * remove csv files and add data link * Update README.md * Update main.py * Update model.py * Update sampler.py * Update utils.py * Update model.py Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>
42 行
1.5 KiB
Markdown
42 行
1.5 KiB
Markdown
# DGL & Pytorch implementation of Enhanced Graph Embedding with Side information (EGES)
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## Version
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dgl==0.6.1, torch==1.9.0
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## Paper
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Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba:
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https://arxiv.org/pdf/1803.02349.pdf
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https://arxiv.org/abs/1803.02349
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## How to run
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Create folder named `data`. Download two csv files from [here](https://github.com/Wang-Yu-Qing/dgl_data/tree/master/eges_data) into the `data` folder.
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Run command: `python main.py` with default configuration, and the following message will shown up:
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```
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Using backend: pytorch
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Num skus: 33344, num brands: 3662, num shops: 4785, num cates: 79
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Epoch 00000 | Step 00000 | Step Loss 0.9117 | Epoch Avg Loss: 0.9117
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Epoch 00000 | Step 00100 | Step Loss 0.8736 | Epoch Avg Loss: 0.8801
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Epoch 00000 | Step 00200 | Step Loss 0.8975 | Epoch Avg Loss: 0.8785
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Evaluate link prediction AUC: 0.6864
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Epoch 00001 | Step 00000 | Step Loss 0.8695 | Epoch Avg Loss: 0.8695
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Epoch 00001 | Step 00100 | Step Loss 0.8290 | Epoch Avg Loss: 0.8643
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Epoch 00001 | Step 00200 | Step Loss 0.8012 | Epoch Avg Loss: 0.8604
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Evaluate link prediction AUC: 0.6875
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...
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Epoch 00029 | Step 00000 | Step Loss 0.7095 | Epoch Avg Loss: 0.7095
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Epoch 00029 | Step 00100 | Step Loss 0.7248 | Epoch Avg Loss: 0.7139
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Epoch 00029 | Step 00200 | Step Loss 0.7123 | Epoch Avg Loss: 0.7134
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Evaluate link prediction AUC: 0.7084
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```
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The AUC of link-prediction task on test graph is computed after each epoch is done.
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## Reference
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https://github.com/nonva/eges
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https://github.com/wangzhegeek/EGES.git
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