The dataset is a copy of the Epinions and Ciao
dataset in the <https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm>
run script below, params are default you can change on main.py to setup model for explore model:
python main.py --model=<name_model> --dataset=<datasetname>
example:
python main.py --model=PMF --dataset=ciao
python main_graphrec.py --data=data/ciao
python main_gcn.py --dataset=ciao --epochs=100
python main_gtn --dataset=ciao
We use 60/20/20 for training, validation, test set
| learning_rate | k_dim | RMSE | MAE |
|---|---|---|---|
| 0.001 | 16 | 1.05318 | 0.81547 |
| 0.005 | 16 | 1.05399 | 0.81527 |
| 0.01 | 16 | 1.05903 | 0.81657 |
| 0.05 | 16 | 1.06514 | 0.82039 |
| 0.05 | 8 | 1.06159 | 0.82062 |
| 0.01 | 8 | 1.06556 | 0.82129 |
| 0.005 | 8 | 1.05626 | 0.81623 |
Ciao Dataset
| Model | RMSE | MAE |
|---|---|---|
| PMF | 1.0581 | 0.8184 |
| NeuMF | 1.0439 | 0.8052 |
| GraphRec | 1.0090 | 0.7834 |
| GCN | 1.0605 | 0.8270 |
| GTN | 0.9732 | 0.7641 |
Epinions Dataset
| Model | RMSE | MAE |
|---|---|---|
| PMF | 1.1829 | 0.9713 |
| NeuMF | 1.1411 | 0.9072 |
| GraphRec | 1.1078 | 0.8524 |
| GCN | 1.1680 | 0.8956 |
| GTN | 1.0139 | 0.8436 |
- Mnih, A., & Salakhutdinov, R. (2007). Probabilistic matrix factorization. In Advances in neural information processing systems (pp. 1257-1264).
- Salakhutdinov, R. Probabilistic matrix factorization in Matlab.