justpartir/nerec

NERec

Code for paper "Neighbor-based Enhanced GNN for Social Recommendation via more Informative Neighbor Aggregation"

github:

• Code for NERec

(due to space limitations, we upload the newest code and whole datasets with our experiments in this link)

Requirements:

requirements.txt

How to use:

First Step

(if you want to change dataset, please use the step)

python preprocess_data.py

or if you use jupyter notebook, you can use "preprocess_data.ipynb";

you will find a new "pkl" file in "data" path (such as "ciao_dense.pkl", we use it in our discussions of EXPERIMENT part)

Second Step

(param_parser.py : each parameter's setting, change the parameter to conduct experiments.)

python Run_NERec_examples.py

or if you use jupyter notebook, you can use "Run_NERec_examples.ipynb".

Tip

Raw Datasets (Ciao and Epinions) can be downloaded at http://www.cse.msu.edu/~tangjili/trust.html

Useful Resources

• Code for some traditional and social recommendation methods
• Code for GraphRec
• Paper summary for social recommendation
• Code for GonsisRec

If you use this code, please cite:

paper link: https://arxiv.org/pdf/2105.02254
@inproceedings{yang2021consisrec,
title={ConsisRec: Enhancing GNN for Social Recommendation viaConsistent Neighbor Aggregation},
author={Yang, Liangwei and Liu, Zhiwei and Dou, Yingtong and Ma, Jing and Philip S. Yu},
journal={Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval},
year={2021},
publisher={ACM}
}

paper link: https://arxiv.org/pdf/1902.07243.pdf
@inproceedings{fan2019graph,
title={Graph neural networks for social recommendation},
author={Fan, Wenqi and Ma, Yao and Li, Qing and He, Yuan and Zhao, Eric and Tang, Jiliang and Yin, Dawei},
booktitle={The World Wide Web Conference},
pages={417--426},
year={2019}
}