The Performance of All Models on the Gowalla Dataset

Rank	Model	Recall@20	NDCG@20	Paper	Year
1	NESCL	0.1917	0.1617	Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering	2024
2	BSPM-EM	0.192	0.1597	Blurring-Sharpening Process Models for Collaborative Filtering	2022
3	BSPM-LM	0.1901	0.157	Blurring-Sharpening Process Models for Collaborative Filtering	2022
4	LT-OCF	0.1875	0.1574	LT-OCF: Learnable-Time ODE-based Collaborative Filtering	2021
5	SimpleX	0.1872	0.1557	SimpleX: A Simple and Strong Baseline for Collaborative Filtering	2021
6	UltraGCN	0.1862	0.158	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	2021
7	Emb-GCN	0.1862		UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	2021
8	GF-CF	0.1849	0.1518	How Powerful is Graph Convolution for Recommendation?	2021
9	LightGCN	0.183	0.1554	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	2020
10	NGCF	0.157		Neural Graph Collaborative Filtering	2019

1. The code repository for the paper: Peijie Sun , Le Wu, Kun Zhang, Xiangzhi Chen, Meng Wang. Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering (Accepted by TKDE).

2. The dataset can refer to following links(Baidu Netdisk, Google Drive).

3. The parameters files locate in config/amazon-book \ gowalla \ yelp2018 directories

4. As we have updated the proposed model name to NESCL, its previous name is SUPCCL, it can be found in the path recbole/model/general_recommender/supccl.py

5. To train the model, you should first prepare the training environment

• pip install -r requirements.txt
• python setup.py build_ext --inplace (We adopt the C++ evaluator in https://github.com/kuandeng/LightGCN)

6. Then, you can execute following commands to train the model based on different datasets:

• python run_recbole_autodl.py --model=SUPCCL --dataset=yelp2018 --config=True --dataloader_file=/root/autodl-fs/yelp2018-for-SUPCCL-dataloader.pth

• python run_recbole_autodl.py --model=SUPCCL --dataset=amazon-book --config=True --dataloader_file=/root/autodl-fs/amazon-book-for-SUPCCL-dataloader.pth

• python run_recbole_autodl.py --model=SUPCCL --dataset=gowalla --config=True --dataloader_file=/root/autodl-fs/gowalla-for-SUPCCL-dataloader.pth

7. The generated log files saved in log directory, and the temporal model parameters can saved in the saved directory.

If you are interested in my work, you can also pay attention to my personal website: https://www.peijiesun.com

You can cite our paper with:

@article{sun2023neighborhood,
  title={Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering},
  author={Sun, Peijie and Wu, Le and Zhang, Kun and Chen, Xiangzhi and Wang, Meng},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2023},
  publisher={IEEE}
}