Fetching the paper…
Reading the bibliography…
Graph neural network (GNN) is widely used for recommendation to model high-order interactions between users and items.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
k-anonymity: A model for protecting privacy
Latanya Sweeney. 2002 · 2002
Earlier work this paper cites.
Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD . 426–434
Yehuda Koren. 2008 · 2008
Earlier work this paper cites.
Probabilistic matrix factorization. In NIPS . 1257–1264
Andriy Mnih and Russ R Salakhutdinov. 2008 · 2008
Earlier work this paper cites.
Collaborative filtering with graph information: Consistency and scalable methods. In NIPS . 2107–2115
Nikhil Rao, Hsiang-Fu Yu, Pradeep K Ravikumar, and Inderjit S Dhillon. 2015 · 2015
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016 · 2016
Earlier work this paper cites.
Gated Graph Sequence Neural Networks. In ICLR
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016 · 2016
Earlier work this paper cites.
Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In ICLR
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data. In AISTATS . 1273–1282
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
Earlier work this paper cites.
Geometric matrix completion with recurrent multi-graph neural networks. In NIPS . 3697–3707
Federico Monti, Michael Bronstein, and Xavier Bresson. 2017 · 2017
Earlier work this paper cites.
Guaranteeing local differential privacy on ultra-low-power systems. In ISCA . 561–574
Woo-Seok Choi, Matthew Tomei, Jose Rodrigo Sanchez Vicarte, Pavan Kumar Hanumolu, and Rakesh Kumar. 2018 · 2018
Earlier work this paper cites.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018 · 2018
Cited alongside, same era.
Privacy enhanced matrix factorization for recommendation with local differential privacy
Hyejin Shin, Sungwook Kim, Junbum Shin, and Xiaokui Xiao. 2018 · 2018
Cited alongside, same era.
Graph Attention Networks. In ICLR
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
Graph convolutional neural networks for web-scale recommender systems. In KDD . 974–983
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2018 · 2018
Ligeng Zhu, Zhijian Liu, and Song Han. 2019 · 2019
Later among the works it cites.
Secure federated matrix factorization
Di Chai, Leye Wang, Kai Chen, and Qiang Yang. 2020 · 2020
Later among the works it cites.
Federated Multi-view Matrix Factorization for Personalized Recommendations
Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy, Kuan Eeik Tan, Suleiman A Khan, and Muhammad Ammad-Ud-Din. 2020 · 2020
Later among the works it cites.
Graph Enhanced Representation Learning for News Recommendation. In WWW . 2863–2869
Suyu Ge, Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2020 · 2020
Later among the works it cites.
Graph neural news recommendation with long-term and short-term interest modeling
Linmei Hu, Chen Li, Chuan Shi, Cheng Yang, and Chao Shao. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Muhammad Ammad, Elena Ivannikova, Suleiman A Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. 2019 · 2019
Cited alongside, same era.
Graph neural networks for social recommendation. In WWW . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Cited alongside, same era.
Decentralized Recommendation Based on Matrix Factorization: A Comparison of Gossip and Federated Learning. In ECML-PKDD . Springer, 317–332
István Hegedűs, Gábor Danner, and Márk Jelasity. 2019 · 2019
Cited alongside, same era.
Sgnn: A graph neural network based federated learning approach by hiding structure. In IEEE Big Data . IEEE, 2560–2568
Guangxu Mei, Ziyu Guo, Shijun Liu, and Li Pan. 2019 · 2019
Cited alongside, same era.
Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019 · 2019
Cited alongside, same era.
STAR-GCN: stacked and reconstructed graph convolutional networks for recommender systems. In IJCAI . AAAI Press, 4264–4270
Jiani Zhang, Xingjian Shi, Shenglin Zhao, and Irwin King. 2019 · 2019
Cited alongside, same era.
Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In KDD . 968–977
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang. 2019c
Cited in the paper.
Federated Dynamic GNN with Secure Aggregation
Meng Jiang, Taeho Jung, Ryan Karl, and Tong Zhao. 2020 · 2020
Later among the works it cites.
Multi-behavior recommendation with graph convolutional networks. In SIGIR . 659–668
Bowen Jin, Chen Gao, Xiangnan He, Depeng Jin, and Yong Li. 2020 · 2020
Later among the works it cites.
FedRec: Privacy-Preserving News Recommendation with Federated Learning
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, and Xing Xie. 2020a · 2020
Later among the works it cites.
Privacy-Preserving News Recommendation Model Training via Federated Learning
Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang, and Xing Xie. 2020b · 2020
Later among the works it cites.
MGAT: Multimodal Graph Attention Network for Recommendation
Zhulin Tao, Yinwei Wei, Xiang Wang, Xiangnan He, Xianglin Huang, and Tat-Seng Chua. 2020 · 2020
Later among the works it cites.
Global Context Enhanced Graph Neural Networks for Session-based Recommendation. In SIGIR . 169–178
Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu. 2020 · 2020
Later among the works it cites.