Graph Convolutional Matrix Completion

Paper link: https://arxiv.org/abs/1706.02263 Author's code: https://github.com/riannevdberg/gc-mc

The implementation does not handle side-channel features and mini-epoching and thus achieves slightly worse performance when using node features.

Credit: Jiani Zhang (@jennyzhang0215)

Dependencies

• MXNet 1.5.0+
• pandas
• gluonnlp

Data

Supported datasets: ml-100k, ml-1m, ml-10m

How to run

ml-100k, no feature

DGLBACKEND=mxnet python train.py --data_name=ml-100k --use_one_hot_fea --gcn_agg_accum=stack

Results: RMSE=0.9077 (0.910 reported) Speed: 0.0246s/epoch (vanilla implementation: 0.1008s/epoch)

ml-100k, with feature

DGLBACKEND=mxnet python train.py --data_name=ml-100k --gcn_agg_accum=stack

Results: RMSE=0.9495 (0.905 reported)

ml-1m, no feature

DGLBACKEND=mxnet python train.py --data_name=ml-1m --gcn_agg_accum=sum --use_one_hot_fea

Results: RMSE=0.8377 (0.832 reported) Speed: 0.0695s/epoch (vanilla implementation: 1.538s/epoch)

ml-10m, no feature

DGLBACKEND=mxnet python train.py --data_name=ml-10m --gcn_agg_accum=stack --gcn_dropout=0.3 \
                                 --train_lr=0.001 --train_min_lr=0.0001 --train_max_iter=15000 \
                                 --use_one_hot_fea --gen_r_num_basis_func=4

Results: RMSE=0.7875 (0.777 reported) Speed: 0.6480s/epoch (vanilla implementation: OOM)

Testbed: EC2 p3.2xlarge