This is our Pytorch implementation for the paper:
Ding Zou, Wei Wei, Feida Zhu, Chuanyu Xu, Tao Zhang, Chengfu Huo (2024). Knowledge Enhanced Multi-intent Transformer Network for Recommendation , Paper in arXiv. In WWW 2024 (Industry Track).
The code has been tested running under Python 3.7.9. The required packages are as follows:
- pytorch == 1.5.0
- numpy == 1.15.4
- scipy == 1.1.0
- sklearn == 0.20.0
- torch_scatter == 2.0.5
- torch_sparse == 0.6.10
- networkx == 2.5
The hyper-parameter search range and optimal settings have been clearly stated in the codes (see the parser function in utils/parser.py).
- Train and Test
python main.py
If you want to use our codes and datasets in your research, please cite:
@inproceedings{
kgtn2024,
title={Knowledge Enhanced Multi-intent Transformer Network for Recommendation},
author={Zou, Ding and Wei, Wei and Zhu, Feida and Xu, Chuanyu and Zhang, Tao and Huo, Chengfu},
booktitle={Proceedings of the ACM Web Conference 2024},
year={2024}
}
We provide three processed datasets: Book-Crossing, MovieLens-1M, and Last.FM.
We follow the paper " Ripplenet: Propagating user preferences on the knowledge graph for recommender systems." to process data.
| Book-Crossing | MovieLens-1M | Last.FM | ||
|---|---|---|---|---|
| User-Item Interaction | #Users | 17,860 | 6,036 | 1,872 |
| #Items | 14,967 | 2,445 | 3,846 | |
| #Interactions | 139,746 | 753,772 | 42,346 | |
| Knowledge Graph | #Entities | 77,903 | 182,011 | 9,366 |
| #Relations | 25 | 12 | 60 | |
| #Triplets | 151,500 | 1,241,996 | 15,518 |
- We partially use the codes of KGIN.
- You could find all other baselines in Github.