Code of paper "Temporal Meta-path Guided Explainable Recommendation".

python==3.6.12
networkx==2.5
numpy==1.15.0
pandas==1.0.1
pytorch==1.0.0
pytorch-nlp==0.5.0
gensim==3.8.3

You can also install the environment via requirements.txt and environment.yaml.

If you want to change the dataset, you can modify the name in the code.

1.process data

python data_process.py

2.learn the user and item representations

python data/path/embed_nodes.py

3.learn the item-item path representations

python data/path/user_history/item_item_representation.py

4.learn the user-item path representations

python data/user_item_representation.py

5.generate user-item and item-item meta-path instances and learn their representations

python data/path/generate_paths.py
python data/path/user_history/meta_path_instances_representation.py

6.sequence item-item paths for each user

python data/path/user_history/user_history.py

7.run the recommendation

python run.py

If you find this code useful in your research, please consider citing:

@article{chen2021temporal,
  title={Temporal Meta-path Guided Explainable Recommendation},
  author={Chen, Hongxu and Li, Yicong and Sun, Xiangguo and Xu, Guandong and Yin, Hongzhi},
  journal={arXiv preprint arXiv:2101.01433},
  year={2021}
}