Paper link: https://arxiv.org/pdf/2108.11124.pdf
Author's code: https://github.com/swtheing/IMC-GAE
The implementation does not handle side-channel features and mini-epoching and thus achieves slightly worse performance when using node features.
Credit: Wei shen (@swtheing)
Latest tested combination: Python 3.8.1 + PyTorch 1.4.0 + DGL 0.5.2.
Install PyTorch
Install DGL
Other required python libraries: numpy, scipy, pandas, h5py, networkx, tqdm ,bidict etc.
Supported datasets: ml-100k, ml-1m, ml-10m, flixster, douban, yahoo_music
To train on Flixster, type:
python -u train.py --data_name=flixster \
--use_one_hot_fea \
--gcn_agg_accum=sum \
--device 0 \
--ARR 0.00000000000 \
--train_early_stopping_patience 200 \
--layers 2 \
--gcn_agg_units 30 \
--train_lr 0.01 \
--data_valid_ratio 0.1 \
--model_activation tanh \
--gcn_out_units 30Results: RMSE=0.8838
To train on douban, type:
python -u train.py --data_name=douban \
--use_one_hot_fea \
--gcn_agg_accum=sum \
--device 0 \
--ARR 0.00000000000 \
--train_early_stopping_patience 1800 \
--layers 5 \
--gcn_agg_units 90 \
--train_lr 0.01 \
--data_valid_ratio 0.05 \
--model_activation tanh \
--gcn_out_units 80Results: RMSE=0.7211
To train on yahoo_music, type:
python -u train.py --data_name=yahoo_music \
--use_one_hot_fea \
--gcn_agg_accum=sum \
--device 0 \
--ARR 0.00000000000 \
--train_early_stopping_patience 20 \
--layers 3 \
--gcn_agg_units 30 \
--train_lr 0.01 \
--data_valid_ratio 0.1 \
--model_activation tanh \
--gcn_out_units 30Results: RMSE=18.7018
To train on MovieLens-100K, type:
python -u train.py --data_name=ml-100k \
--use_one_hot_fea \
--device 0 \
--ARR 0.00004 \
--layers 2 \
--data_valid_ratio 0.05 \
--model_activation tanh Results: RMSE=0.8967
To train on MovieLens-1M, type:
python -u train.py --data_name=ml-1m \
--use_one_hot_fea \
--device 0 \
--ARR 0.000004 \
--layers 2 \
--data_valid_ratio 0.05 \
--model_activation tanh Results: RMSE=0.8290
If you find the code useful, please cite our paper.
@article{shen2021inductive,
title={Inductive Matrix Completion Using Graph Autoencoder},
author={Shen, Wei and Zhang, Chuheng and Tian, Yun and Zeng, Liang and He, Xiaonan and Dou, Wanchun and Xu, Xiaolong},
journal={arXiv preprint arXiv:2108.11124},
year={2021}
}
9/17/2021