This repository provides the code for the paper titled Self Contrastive Learning for Session-based Recommendation, making the integration of our code contributions into other projects more accessible.
You can reproduce the experiments of our paper Self Contrastive Learning for Session-based Recommendation. We implement three baseline approaches, including
- Global Context Enhanced Graph Neural Networks for Session-based Recommendation, SIGIR 2020
- Self-Supervised Graph Co-Training for Session-based Recommendation, CIKM 2021
- Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation, AAAI 2021
and evaluate them on three datasets, including TMALL, diginetica, and Nowplaying.
Please refer to the repository of each baseline approach (GCE-GNN, COTREC, and DHCN) for the installation and requirements.
We provide datasets in the data folder in each baseline folder, including GCE-GNN, COTREC, and DHCN.
Please refer to the README.md in each baseline folder (GCE-GNN, COTREC, and DHCN) for the instructions to run the code.
If you have any questions regarding the code or the paper, please feel free to reach out to Zhengxiang at [email protected]. If you experience any difficulties while using the code or need to report a bug, feel free to open an issue. We kindly ask that you provide detailed information about the problem to help us provide effective support.
@inproceedings{shi2023self,
title = {Self Contrastive Learning for Session-based Recommendation},
author = {Shi, Zhengxiang and Xi, Wang and Lipani, Aldo},
publisher = {Springer},
address = {Glasgow, Scotland},
booktitle={European Conference on Information Retrieval (ECIR 2024)},
url = {https://arxiv.org/abs/2306.01266},
year = {2023},
}
This repository is built upon the following repositories: