Fetching the paper…
Reading the bibliography…
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF).
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM . 1893–1902
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, et al · 1902
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization. In ICLR
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
Autorec: Autoencoders meet collaborative filtering. In WWW . 111–112
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015 · 2015
Earlier work this paper cites.
Some new estimates of the ’Jensen gap’
Shoshana Abramovich and Lars-Erik Persson. 2016 · 2016
Earlier work this paper cites.
Neural collaborative filtering. In WWW . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Bounds on the Jensen gap, and implications for mean-concentrated distributions
Xiang Gao, Meera Sitharam, and Adrian E Roitberg. 2019 · 2019
Earlier work this paper cites.
Session-based social recommendation via dynamic graph attention networks. In WSDM . 555–563
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019 · 2019
Earlier work this paper cites.
Deep Graph Infomax.. In ICLR
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019 · 2019
Earlier work this paper cites.
Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020b · 2020
Earlier work this paper cites.
Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR . 639–648
Xiangnan He, Kuan Deng, Xiang Wang, et al · 2020
Earlier work this paper cites.
Graph representation learning via graphical mutual information maximization. In WWW . 259–270
Zhen Peng, Wenbing Huang, Minnan Luo, et al · 2020
Earlier work this paper cites.
Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, et al · 2020
Cited alongside, same era.
Sequential recommendation with graph neural networks. In SIGIR . 378–387
Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin, and Yong Li. 2021 · 2021
Cited alongside, same era.
Disentangled contrastive learning on graphs
Haoyang Li, Xin Wang, Ziwei Zhang, Zehuan Yuan, Hang Li, and Wenwu Zhu. 2021 · 2021
Cited alongside, same era.
Learning to drop: Robust graph neural network via topological denoising. In WSDM . 779–787
Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang. 2021 · 2021
Cited alongside, same era.
Knowledge-Guided Disentangled Representation Learning for Recommender Systems
Shanlei Mu, Yaliang Li, Wayne Xin Zhao, Siqing Li, and Ji-Rong Wen. 2021 · 2021
Cited alongside, same era.
HGCF: Hyperbolic Graph Convolution Networks for Collaborative Filtering. In WWW . 593–601
Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering. In SIGIR . 122–132
Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, and Wayne Xin Zhao. 2022 · 2022
Later among the works it cites.
Profiling the Design Space for Graph Neural Networks based Collaborative Filtering. In WSDM . 1109–1119
Zhenyi Wang, Huan Zhao, and Chuan Shi. 2022 · 2022
Later among the works it cites.
Contrastive meta learning with behavior multiplicity for recommendation. In WSDM . 1120–1128
Wei Wei, Chao Huang, Lianghao Xia, Yong Xu, Jiashu Zhao, and Dawei Yin. 2022 · 2022
Later among the works it cites.
Hypergraph contrastive collaborative filtering. In SIGIR . 70–79
Lianghao Xia, Chao Huang, Yong Xu, Jiashu Zhao, Dawei Yin, and Jimmy Huang. 2022 · 2022
Later among the works it cites.
Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In KDD . 2263–2274
Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, and Chenliang Li. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jianing Sun, Zhaoyue Cheng, Saba Zuberi, Felipe Pérez, and Maksims Volkovs. 2021 · 2021
Cited alongside, same era.
Self-supervised graph learning for recommendation. In SIGIR . 726–735
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, et al · 2021
Cited alongside, same era.
Enhanced graph learning for collaborative filtering via mutual information maximization. In SIGIR . 71–80
Yonghui Yang, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2021 · 2021
Cited alongside, same era.
Self-supervised Learning for Large-scale Item Recommendations. In CIKM . 4321–4330
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, et al · 2021
Cited alongside, same era.
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation. In WWW . 413–424
Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang. 2021 · 2021
Cited alongside, same era.
Intent contrastive learning for sequential recommendation. In WWW . 2172–2182
Yongjun Chen, Zhiwei Liu, Jia Li, Julian McAuley, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning. In WWW . 2320–2329
Zihan Lin, Changxin Tian, Yupeng Hou, and Wayne Xin Zhao. 2022 · 2022
Cited alongside, same era.
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation. In WWW . 2216–2226
Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu, Fuli Feng, Zhou Zhao, Tat-seng Chua, and Fei Wu. 2022 · 2022
Later among the works it cites.
Multi-view intent disentangle graph networks for bundle recommendation
Sen Zhao, Wei Wei, Ding Zou, and Xianling Mao. 2022 · 2022
Later among the works it cites.
Mutually-regularized dual collaborative variational auto-encoder for recommendation systems. In WWW . 2379–2387
Yaochen Zhu and Zhenzhong Chen. 2022 · 2022
Later among the works it cites.
Improving knowledge-aware recommendation with multi-level interactive contrastive learning. In CIKM . 2817–2826
Ding Zou, Wei Wei, Ziyang Wang, Xian-Ling Mao, Feida Zhu, Rui Fang, and Dangyang Chen. 2022 · 2022
Later among the works it cites.
LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In ICLR
Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023 · 2023
Closest in time.
Heterogeneous Graph Contrastive Learning for Recommendation. In WSDM . 544–552
Mengru Chen, Chao Huang, Lianghao Xia, Wei Wei, Yong Xu, and Ronghua Luo. 2023 · 2023
Closest in time.
Automated Self-Supervised Learning for Recommendation. In WWW . 992–1002
Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, and Ben Kao. 2023 · 2023
Closest in time.