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Negative sampling approaches are prevalent in implicit collaborative filtering for obtaining negative labels from massive unlabeled data.
Learning classifiers from only positive and unlabeled data
Charles Elkan and Keith Noto · 2008
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Factorization meets the neighborhood: a multifaceted collaborative filtering model
Yehuda Koren · 2008
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One-class collaborative filtering
Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Bpr: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme · 2009
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Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
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Translating embeddings for modeling multi-relational data
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Optimizing top-n collaborative filtering via dynamic negative item sampling
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu · 2013
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Analysis of learning from positive and unlabeled data
Marthinus C Du Plessis, Gang Niu, and Masashi Sugiyama · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Probabilistic matrix factorization with non-random missing data
José Miguel Hernández-Lobato, Neil Houlsby, and Zoubin Ghahramani · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Improving pairwise learning for item recommendation from implicit feedback
Steffen Rendle and Christoph Freudenthaler · 2014
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen · 2014
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Learning image and user features for recommendation in social networks
Xue Geng, Hanwang Zhang, Jingwen Bian, and Tat-Seng Chua · 2015
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Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
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Stochastic optimization with importance sampling for regularized loss minimization
Peilin Zhao and Tong Zhang · 2015
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Modeling user exposure in recommendation
Dawen Liang, Laurent Charlin, James McInerney, and David M Blei · 2016
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Mixture proportion estimation via kernel embeddings of distributions
Harish Ramaswamy, Clayton Scott, and Ambuj Tewari · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
Kbgan: Adversarial learning for knowledge graph embeddings
Liwei Cai and William Yang Wang · 2018
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Self-paced network embedding
Hongchang Gao and Heng Huang · 2018
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Efficient training for positive unlabeled learning
Emanuele Sansone, Francesco GB De Natale, and Zhi-Hua Zhou · 2018
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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On sampling strategies for neural network-based collaborative filtering
Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong · 2017
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Class-prior estimation for learning from positive and unlabeled data
Marthinus C Du Plessis, Gang Niu, and Masashi Sugiyama · 2017
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Neural collaborative filtering
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua · 2017
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Positive-unlabeled learning with non-negative risk estimator
Ryuichi Kiryo, Gang Niu, Marthinus C du Plessis, and Masashi Sugiyama · 2017
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Interpreting user inaction in recommender systems
Qian Zhao, Martijn C Willemsen, Gediminas Adomavicius, F Maxwell Harper, and Joseph A Konstan · 2018
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Reinforced negative sampling for recommendation with exposure data
Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin · 2019
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Adversarial sampling and training for semi-supervised information retrieval
Dae Hoon Park and Yi Chang · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Neural graph collaborative filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua · 2019
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Noise contrastive estimation for one-class collaborative filtering
Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, and Himanshu Rai · 2019
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Nscaching: Simple and efficient negative sampling for knowledge graph embedding
Yongqi Zhang, Quanming Yao, Yingxia Shao, and Lei Chen · 2019
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Efficient neural matrix factorization without sampling for recommendation
Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma · 2020
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Understanding negative sampling in graph representation learning
Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang · 2020
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