STAR-GCN

• pytorch & dgl implementation of Stacked and Reconstructed GCN for Recommender Systems
• paper : STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

✅ ML-100K

Usage

python main.py --data_name=ml-100k train --iteration=2000 --in_feats_dim=32

Results

model : 2b1l -rm

Best iter : 1400
Best valid RMSE : 0.9013
Best test RMSE : 0.9150

model : 2b1l

reported RMSE : 0.8950

✅ ML-1M

Usage

python main.py --data_name=ml-1m train --iteration=2000 --in_feats_dim=64

Results

model : 2b1l -rm

Best iter : 1990
Best valid RMSE : 0.8565
Best test RMSE : 0.8547

model : 2b1l

reported RMSE : 0.833

Notes

• only transductive rating prediction is available

TODO

• apply sample-and-remove strategy and change model 2b1l -rm to 2b1l
• implement inductive rating prediction
• masked learning
• mini-batch learning