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>
DGL & Pytorch implementation of Enhanced Graph Embedding with Side information (EGES)
Version
dgl==0.6.1, torch==1.9.0
Paper
Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba:
https://arxiv.org/pdf/1803.02349.pdf
https://arxiv.org/abs/1803.02349
How to run
Create folder named data. Download two csv files from here into the data folder.
Run command: python main.py with default configuration, and the following message will shown up:
Using backend: pytorch
Num skus: 33344, num brands: 3662, num shops: 4785, num cates: 79
Epoch 00000 | Step 00000 | Step Loss 0.9117 | Epoch Avg Loss: 0.9117
Epoch 00000 | Step 00100 | Step Loss 0.8736 | Epoch Avg Loss: 0.8801
Epoch 00000 | Step 00200 | Step Loss 0.8975 | Epoch Avg Loss: 0.8785
Evaluate link prediction AUC: 0.6864
Epoch 00001 | Step 00000 | Step Loss 0.8695 | Epoch Avg Loss: 0.8695
Epoch 00001 | Step 00100 | Step Loss 0.8290 | Epoch Avg Loss: 0.8643
Epoch 00001 | Step 00200 | Step Loss 0.8012 | Epoch Avg Loss: 0.8604
Evaluate link prediction AUC: 0.6875
...
Epoch 00029 | Step 00000 | Step Loss 0.7095 | Epoch Avg Loss: 0.7095
Epoch 00029 | Step 00100 | Step Loss 0.7248 | Epoch Avg Loss: 0.7139
Epoch 00029 | Step 00200 | Step Loss 0.7123 | Epoch Avg Loss: 0.7134
Evaluate link prediction AUC: 0.7084
The AUC of link-prediction task on test graph is computed after each epoch is done.