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We propose a new STAcked and Reconstructed Graph Convolutional Networks (STAR-GCN) architecture to learn node representations for boosting the performance in recommender systems, especially in the cold start scenario.
Exact matrix completion via convex optimization
Emmanuel J Candès and Benjamin Recht · 2009
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Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky · 2009
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Cold-start problem in collaborative recommender systems: efficient methods based on ask-to-rate technique
Mohammad-Hossein Nadimi-Shahraki and Mozhde Bahadorpour · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Neural network matrix factorization
Gintare Karolina Dziugaite and Daniel M Roy · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
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Collaborative filtering with graph information: Consistency and scalable methods
Nikhil Rao, Hsiang-Fu Yu, Pradeep K Ravikumar, and Inderjit S Dhillon · 2015
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Autorec: Autoencoders meet collaborative filtering
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie · 2015
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Collaborative deep learning for recommender systems
Hao Wang, Naiyan Wang, and Dit-Yan Yeung · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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The movielens datasets: History and context
F Maxwell Harper and Joseph A Konstan · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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A neural autoregressive approach to collaborative filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding, and Hanning Zhou · 2016
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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Dropoutnet: Addressing cold start in recommender systems
Maksims Volkovs, Guangwei Yu, and Tomi Poutanen · 2017
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Thread popularity prediction and tracking with a permutation-invariant model
Hou Pong Chan and Irwin King · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Difficulty controllable question generation for reading comprehension
Yifan Gao, Jianan Wang, Lidong Bing, Irwin King, and Michael R Lyu · 2018
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Deep models of interactions across sets
Jason S. Hartford, Devon R. Graham, Kevin Leyton-Brown, and Siamak Ravanbakhsh · 2018
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Geometric matrix completion with recurrent multi-graph neural networks
Federico Monti, Michael Bronstein, and Xavier Bresson · 2017
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Learning to rank using localized geometric mean metrics
Yuxin Su, Irwin King, and Michael R. Lyu · 2017
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Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
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Machine learning for spatiotemporal sequence forecasting: A survey
Xingjian Shi and Dit-Yan Yeung · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung · 2018
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