Paper link: https://arxiv.org/abs/1904.12058 Author's code: https://github.com/muhanzhang/IGMC
- PyTorch 1.2+
- DGL 0.5 (nightly version)
Supported datasets: ml-100k, ml-1m
- ml-100k
python3 train.py --data_name ml-100k --testing \
--batch_size 32 --edge_dropout 0.2 --max_nodes_per_hop 200 --train_epochs 80 \
--device 0- ml-1m
python3 train_multi_gpu.py --data_name ml-1m --testing \
--batch_size 32 --edge_dropout 0. --max_nodes_per_hop 100 --train_epochs 40 \
--train_log_interval 1000 --valid_log_interval 5 --train_lr_decay_step 20 \
--gpu 0,1,2,3| Dataset | Our code best of epochs |
Author code best of epochs / ensembled |
|---|---|---|
| ml-100k | 0.9053 | 0.9053 / 0.9051 |
| ml-1m | 0.8679 | 0.8685 / 0.8558 |
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