# XSimNGCL-master **Repository Path**: qipengbi/XSimNGCL-master ## Basic Information - **Project Name**: XSimNGCL-master - **Description**: XSimGCL+NCL - **Primary Language**: Python - **License**: MulanPSL-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-03-13 - **Last Updated**: 2023-05-23 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README <<<<<<< HEAD # XsimNGCL ## Requirements ``` recbole==1.0.0 python==3.7.7 pytorch==1.7.1 faiss-gpu==1.7.1 cudatoolkit==10.1 ``` ## Quick Start ```bash python main.py --dataset ml-1m ``` You can replace `ml-1m` to `yelp`, `amazon-books`, `gowalla-merged` or `alibaba` to reproduce the results reported in our paper. ## Datasets For `alibaba`, you can download `alibaba.zip` from [Google Drive](https://drive.google.com/file/d/1Th7ii_Z0l6AjGq8zWsKuLVCsacIO1AQJ/view?usp=sharing). Then, ```bash mkdir dataset mv alibaba.zip dataset unzip alibaba.zip python main.py --dataset alibaba ``` For others, they will be downloaded automatically via RecBole once you run the main program. Take `yelp` for example, ```bash python main.py --dataset yelp ``` ## Customized datasets To run NCL on customized datasets, please following https://github.com/RUCAIBox/NCL/issues/1#issuecomment-1076370560. ## Acknowledgement The implementation is based on the open-source recommendation library [RecBole](https://github.com/RUCAIBox/RecBole). Please cite the following papers as the references if you use our codes or the processed datasets. ``` @inproceedings{lin2022ncl, author={Zihan Lin and Changxin Tian and Yupeng Hou and Wayne Xin Zhao}, title={Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning}, booktitle={{WWW}}, year={2022}, } @inproceedings{zhao2021recbole, title={Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms}, author={Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Kaiyuan Li and Yushuo Chen and Yujie Lu and Hui Wang and Changxin Tian and Xingyu Pan and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji-Rong Wen}, booktitle={{CIKM}}, year={2021} } ``` ======= # XSimNGCL-master #### 介绍 {**以下是 Gitee 平台说明,您可以替换此简介** Gitee 是 OSCHINA 推出的基于 Git 的代码托管平台(同时支持 SVN)。专为开发者提供稳定、高效、安全的云端软件开发协作平台 无论是个人、团队、或是企业,都能够用 Gitee 实现代码托管、项目管理、协作开发。企业项目请看 [https://gitee.com/enterprises](https://gitee.com/enterprises)} #### 软件架构 软件架构说明 #### 安装教程 1. xxxx 2. xxxx 3. xxxx #### 使用说明 1. xxxx 2. xxxx 3. xxxx #### 参与贡献 1. Fork 本仓库 2. 新建 Feat_xxx 分支 3. 提交代码 4. 新建 Pull Request #### 特技 1. 使用 Readme\_XXX.md 来支持不同的语言,例如 Readme\_en.md, Readme\_zh.md 2. Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com) 3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目 4. [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目,是综合评定出的优秀开源项目 5. Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help) 6. Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) >>>>>>> 9197ba5929b42b3cfe0622f8d80016e43f1d429a