This is the PyTorch implementation for DiffKG proposed in the paper DiffKG: Knowledge Graph Diffusion Model for Recommendation, which is accepted by WSDM 2024 Oral.
Yangqin Jiang, Yuhao Yang, Lianghao Xia, and Chao Huang. 2024. DiffKG: Knowledge Graph Diffusion Model for Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM ’24), March 4–8, 2024, Merida, Mexico. ACM, New York, NY, USA, 9 pages.
In this paper, we propose a novel knowledge graph diffusion model for recommendation, referred to as DiffKG. Our framework integrates a generative diffusion model with a data augmentation paradigm, enabling robust knowledge graph representation learning. This integration facilitates a better alignment between knowledge-aware item semantics and collaborative relation modeling. Moreover, we introduce a collaborative knowledge graph convolution mechanism that incorporates collaborative signals reflecting user-item interaction patterns, guiding the knowledge graph diffusion process.
We develop our codes in the following environment:
- python==3.9.13
- numpy==1.23.1
- torch==1.11.0
- scipy==1.9.1
| Statistics | Last-FM | MIND | Alibaba-iFashion |
|---|---|---|---|
| # Users | 23,566 | 100,000 | 114,737 |
| # Items | 48,123 | 30,577 | 30,040 |
| # Interactions | 3,034,796 | 2,975,319 | 1,781,093 |
| # Density | 2.7 × |
9.7 × |
5.2 × |
| Knowledge Graph | |||
| # Entities | 58,266 | 24,733 | 59,156 |
| # Relations | 9 | 512 | 51 |
| # Triplets | 464,567 | 148,568 | 279,155 |
The command lines to train DiffKG on the three datasets are as below. The un-specified hyperparameters in the commands are set as default.
- Last-FM
python Main.py --data lastfm --reg 1e-5 --mess_dropout_rate 0.2 --layer_num_kg 2 --res_lambda 0 --triplet_num -1 --cl_pattern 1 --keepRate 0.1 --e_loss 0.01- MIND
python Main.py --data mind --e_loss 0.1 --temp 1 --ssl_reg 1 --mess_dropout_rate 0.2 --res_lambda 1- Alibaba-iFashion
python Main.py --data alibaba --reg 1e-6 --epoch 50 --ssl_reg 1 --temp 1If you find this work helpful to your research, please kindly consider citing our paper.
@article{jiang2023diffkg,
title={DiffKG: Knowledge Graph Diffusion Model for Recommendation},
author={Jiang, Yangqin and Yang, Yuhao and Xia, Lianghao and Huang, Chao},
journal={arXiv preprint arXiv:2312.16890},
year={2023}
}