Kaggle知识点：对比学习基础
一文梳理自监督对比学习范式

一、基础

1.1 图神经网络—-Graph Neural Network

GIN: How Powerful are Graph Neural Networks?

  • Keyulu Xu, Weihua Hu, Jure Leskovec, et al. How Powerful are Graph Neural Networks?[C]. In ICLR 2019.

Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks

  • Xianfeng Tang, Huaxiu Yao, Yiwei Sun, et al. Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks[C]. In CIKM 2020.

Strategies for Pre-training Graph Neural Networks

  • Weihua Hu, Bowen Liu, Joseph Gomes, et al. Strategies for Pre-training Graph Neural Networks[C]. In ICLR 2020.

    1.2 自监督

    CPC: Representation Learning with Contrastive Predictive Coding InfoNCE

  • Aaron van den Oord, Yazhe Li, Oriol Vinyals. Representation Learning with Contrastive Predictive Coding[J]. In ArXiv 2018.

DIM: Learning deep representations by mutual information estimation and maximization

  • R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, et al. Learning deep representations by mutual information estimation and maximization[C]. In ICLR 2019.

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

  • Zhenzhong Lan, Mingda Chen, Sebastian Goodman, et al. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations[C]. In ICLR 2020.

SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

GCA: Graph Contrastive Learning with Adaptive Augmentation

  • Yanqiao Zhu, Yichen Xu, Feng Yu, et al. Graph Contrastive Learning with Adaptive Augmentation[C]. In WWW 2021.

GraphCL: Graph Contrastive Learning with Augmentations

  • Yuning You, Tianlong Chen, Yongduo Sui, et al. Graph Contrastive Learning with Augmentations[C]. In NeurIPS 2020.

GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

  • Jiezhong Qiu, Qibin Chen, Yuxiao Dong, et al. GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training[C]. In KDD 2020.

Contrastive Multi-View Representation Learning on Graphs

  • MVGRL
  • Kaveh Hassani, Amir Hosein Khasahmadi. Contrastive Multi-View Representation Learning on Graphs[C]. In ICML 2020.

When Does Self-Supervision Help Graph Convolutional Networks?

  • Yuning You, Tianlong Chen, Zhangyang Wang, et al. When Does Self-Supervision Help Graph Convolutional Networks?[C]. In ICML 2020.

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

  • Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, et al. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization[C]. In ICLR 2020.

DGI: Deep Graph Infomax

  • Petar Velickovic, William Fedus, William L. Hamilton, et al. Deep Graph Infomax[C]. In ICLR 2019.

  • GraphCL翻译

    1.4 推荐

    LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

  • Xiangnan He, Kuan Deng, Xiang Wang, et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation[C]. In SIGIR 2020.

Bias and Debias in Recommender System: A Survey and Future Directions

  • Jiawei Chen, Hande Dong, Xiang Wang, et al. Bias and Debias in Recommender System: A Survey and Future Directions[J]. In ArXiv 2020.

Denoising Implicit Feedback for Recommendation

Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation

Self-supervised learning for large-scale item recommendations

  • Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, et al. Self-supervised learning for large-scale item recommendations[C]. In CIKM 2021.

Google提出用对比学习解决推荐系统长尾问题 对白的算法屋
大规模推荐系统的自监督学习 雨石记
CIKM 2021 | Google出品：将对比学习用于解决推荐系统长尾问题
CIKM2021 | 将对比学习用于解决推荐系统长尾问题
CIKM’21 | 谷歌：推荐中的自监督对比学习
谷歌自监督工作精读
Self-supervised learning for large-scale item recommendations

  • Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, et al. Self-supervised learning for large-scale item recommendations[C]. In CIKM 2021.

Multi-Sample based Contrastive Loss for Top-k Recommendation

  • Hao Tang, Guoshuai Zhao, Yuxia Wu, et al. Multi-Sample based Contrastive Loss for Top-k Recommendation[J]. In IEEE Transactions on Multimedia 2021.

SelfCF: A Simple Framework for Self-supervised Collaborative Filtering

  • Xin Zhou, Aixin Sun, Yong Liu, et al. SelfCF: A Simple Framework for Self-supervised Collaborative Filtering[J]. In ArXiv 2021.

Contrastive Learning for Recommender System

  • Zhuang Liu, Yunpu Ma, Yuanxin Ouyang, et al. Contrastive Learning for Recommender System[C]. In KDD 2021.

    2.2 图神经网络—-Graph Neural Network

    SGL: Self-supervised Graph Learning for Recommendation

  • Jiancan Wu, Xiang Wang, Fuli Feng, et al. Self-supervised Graph Learning for Recommendation[C]. In SIGIR 2021.

[SIGIR’21]SGL: 基于图自监督学习的推荐系统 互联网推荐一点通
SIGIR’21 微软|基于自监督图学习的召回方法 秋叶学习笔记
SIGIR’21|SGL基于图自监督学习的推荐系统 蘑菇先生学习记
Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization

  • Yonghui Yang, Le Wu, Richang Hong, et al. Enhanced Graph Learning for Collaborative Filtering via Mutual Information Maximization[C]. In SIGIR 2021.

SIGIR’21推荐系统挖掘隐式交互，利用互信息进行图学习增强

2.3 序列推荐—-Sequential Recommendation

S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

  • Kun Zhou, Hui Wang, Wayne Xin Zhao, et al. S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization[C]. In CIKM 2020.

Disentangled Self-Supervision in Sequential Recommenders

  • Jianxin Ma, Chang Zhou, Hongxia Yang, et al. Disentangled Self-Supervision in Sequential Recommenders[C]. In KDD 2020.

Contrastive Learning for Sequential Recommendation

  • Xu Xie, Fei Sun, Zhaoyang Liu, et al. Contrastive Learning for Sequential Recommendation[C]. In SIGIR 2021.

UPRec: User-Aware Pre-training for Recommender Systems

  • Chaojun Xiao, Ruobing Xie, Yuan Yao, et al. UPRec: User-Aware Pre-training for Recommender Systems[J]. In ArXiv 2021.

Contrastive Pre-training for Sequential Recommendation

  • Xu Xie, Fei Sun, Zhaoyang Liu, et al. Contrastive Pre-training for Sequential Recommendation[C]. In WWW 2021.