Awesome

Dynamic Graph Evolution Learning for Recommendation

This repository contains PyTorch codes and datasets for the paper:

Haoran Tang, Shiqing Wu, Guandong Xu, and Qing Li. 2023. Dynamic Graph Evolution Learning for Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 1589–1598. https://doi.org/10.1145/3539618.3591674.

Introduction

Dynamic Graph Evolution Learning (DGEL) aims to solve the graph-based real-time evolution problem for multi-round recommendation scenario by three efficient learning modules and also bridge the normalization with model learning by re-scaling enhancement network for better capturing the dynamics.

Citation

If you want to use our codes or refer our paper, please cite it:

@inproceedings{10.1145/3539618.3591674,
author = {Tang, Haoran and Wu, Shiqing and Xu, Guandong and Li, Qing},
title = {Dynamic Graph Evolution Learning for Recommendation},
year = {2023},
isbn = {9781450394086},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3539618.3591674},
doi = {10.1145/3539618.3591674},
pages = {1589–1598},
numpages = {10},
location = {Taipei, Taiwan},
series = {SIGIR '23}
}

Run the codes

Take the Wikipedia dataset as example. Run the following commands:

CUDA_VISIBLE_DEVICES=0 python main.py --dataset wikipedia --embedding_dim 32 --sample_length 100 --epochs 30
CUDA_VISIBLE_DEVICES=0 python evaluate_all.py --dataset wikipedia --embedding_dim 32 --sample_length 100 --epochs 30

--dataset: indicating the data name you select <br>--embedding_dim: diminsion of each dynamic sub-embedding <br>--sample_length: the number of neighbors <br>--epochs: the number of training epochs <br>Note: There are some other hyper-parameters in codes but we have set the optimal values.

Acknowledgements

This research is supported by the dual degree PhD program from the Department of Computing at the Hong Kong Polytechnic University (PolyU) & the School of Computer Science at the University of Technology Sydney (UTS).