# NESCL

**Repository Path**: mktb_nobug/NESCL

## Basic Information

- **Project Name**: NESCL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-08-05
- **Last Updated**: 2024-08-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/neighborhood-enhanced-supervised-contrastive/collaborative-filtering-on-yelp2018)](https://paperswithcode.com/sota/collaborative-filtering-on-yelp2018?p=neighborhood-enhanced-supervised-contrastive)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/neighborhood-enhanced-supervised-contrastive/recommendation-systems-on-gowalla)](https://paperswithcode.com/sota/recommendation-systems-on-gowalla?p=neighborhood-enhanced-supervised-contrastive)


# 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](https://arxiv.org/pdf/2402.11523) | 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](https://arxiv.org/abs/2402.11523)**  (Accepted by TKDE).

2. The dataset can refer to following links([Baidu Netdisk](https://pan.baidu.com/s/1HXFrGavcvGzHzbkIQP_v3w?pwd=ct9x), [Google Drive](https://drive.google.com/drive/folders/1coHwFat2b4prNPQ4Q8QHznbg8rsi6-P1?usp=sharing)). 

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}
}
```