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Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors.
Video suggestion and discovery for youtube: taking random walks through the view graph. In WWW . 895–904
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Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD . 426–434
Yehuda Koren. 2008 · 2008
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Power-Law Distributions in Empirical Data
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BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI . 452–461
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Understanding the difficulty of training deep feedforward neural networks. In AISTATS , Vol. 9. 249–256
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS , Vol. 9. 297–304
Michael Gutmann and Aapo Hyvärinen. 2010 · 2010
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Power law distributions in information science: Making the case for logarithmic binning
Stasa Milojevic. 2010 · 2010
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Distributed Representations of Words and Phrases and their Compositionality. In NIPS . 3111–3119
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Optimizing top-n collaborative filtering via dynamic negative item sampling. In SIGIR . 785–788
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013 · 2013
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Improving pairwise learning for item recommendation from implicit feedback. In WSDM . 273–282
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Context-Aware Friend Recommendation for Location Based Social Networks using Random Walk. In WWW . 531–536
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Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW . 507–517
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Conditional Image Generation with PixelCNN Decoders. In NIPS . 4790–4798
Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, and Alex Graves. 2016 · 2016
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A Generic Coordinate Descent Framework for Learning from Implicit Feedback. In WWW . 1341–1350
Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017 · 2017
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Neural Message Passing for Quantum Chemistry. In ICML , Vol. 70. 1263–1272
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017 · 2017
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Inductive Representation Learning on Large Graphs. In NeurIPS . 1024–1034
William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
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Neural Collaborative Filtering. In WWW . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
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Social Collaborative Viewpoint Regression with Explainable Recommendations. In WSDM . 485–494
Zhaochun Ren, Shangsong Liang, Piji Li, Shuaiqiang Wang, and Maarten de Rijke. 2017 · 2017
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Graph Convolutional Matrix Completion
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Unsupervised Representation Learning by Predicting Image Rotations. In ICLR
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NAIS: Neural Attentive Item Similarity Model for Recommendation
Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018 · 2018
Cited alongside, same era.
How Powerful are Graph Neural Networks?. In ICLR
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Later among the works it cites.
Bias and Debias in Recommender System: A Survey and Future Directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2020a · 2020
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020b · 2020
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Contrastive Multi-View Representation Learning on Graphs
Kaveh Hassani and Amir Hosein Khasahmadi. 2020 · 2020
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Strategies for Pre-training Graph Neural Networks. In ICLR
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec. 2020 · 2020
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Variational Autoencoders for Collaborative Filtering. In WWW . 689–698
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018 · 2018
Cited alongside, same era.
Representation Learning with Contrastive Predictive Coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals. 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.
Unsupervised Feature Learning via Non-Parametric Instance Discrimination. In CVPR . 3733–3742
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. 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.
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. In WWW . 151–161
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Supervised Contrastive Learning. In NeurIPS
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
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GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. In KDD . 1150–1160
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020 · 2020
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InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. 2020 · 2020
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Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks. In CIKM
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang. 2020 · 2020
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Self-supervised Learning for Deep Models in Recommendations
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix X. Yu, Aditya Krishna Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, and Evan Ettinger. 2020 · 2020
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Parameter-efficient transfer from sequential behaviors for user modeling and recommendation. In SIGIR . 1469–1478
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, and Liguang Zhang. 2020 · 2020
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Sˆ3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. In CIKM
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 2020
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Graph Contrastive Learning with Adaptive Augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020 · 2020
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Denoising Implicit Feedback for Recommendation. In WSDM
Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, and Tat-Seng Chua. 2021 · 2021
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