This is PyTorch implementation of the paper:
Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu and Shaoping Ma. 2020. Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation. In SIGIR'20.
You can find TensorFlow implementation by the paper authors here
Please cite the following paper if you use their codes.
@inproceedings{chen2020jointly,
title={Jointly Non-Sampling Learning for Knowledge Graph Enhanced Recommendation},
author={Chen, Chong and Zhang, Min and Ma, Weizhi and Liu, Yiqun and Ma, Shaoping},
booktitle={Proceedings of SIGIR},
year={2020},
}
You also need to cite the KDD'19 paper if you use the datasets.
@inproceedings{KGAT19,
author = {Xiang Wang and
Xiangnan He and
Yixin Cao and
Meng Liu and
Tat{-}Seng Chua},
title = {{KGAT:} Knowledge Graph Attention Network for Recommendation},
booktitle = {{KDD}},
pages = {950--958},
year = {2019}
}
The code has been tested under Python 3.8.5, on Linux system.
Required packages are as follows:
pytorch == 1.7.1
numpy == 1.19.2
pandas == 1.2.1
scikit-learn == 0.23.2
matplotlib == 3.3.2
- Edit parser_jnskr.py to customize your training arguments.
python main.py- Logs will be saved to
./log/JNSKR/
| Model | Best Epoch | Recall@10 | Recall@20 | Recall@40 | NDCG@10 | NDCG@20 | NDCG@40 |
|---|---|---|---|---|---|---|---|
| JNSKR-pytorch | 30 | 0.1079 | 0.1572 | 0.2194 | 0.0938 | 0.1165 | 0.1412 |
Refer to log77.log for more details.
| Model | Best Epoch | Recall@10 | Recall@20 | Recall@40 | NDCG@10 | NDCG@20 | NDCG@40 |
|---|---|---|---|---|---|---|---|
| JNSKR-pytorch | 65 | 0.0453 | 0.0750 | 0.1209 | 0.0808 | 0.1081 | 0.1430 |
Refer to log0.log for more details.