Fetching the paper…
Reading the bibliography…
Geometric matrix completion (GMC) has been proposed for recommendation by integrating the relationship (link) graphs among users/items into matrix completion (MC).
Spectral graph theory
F. R. K. Chung · 1997
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Empirical analysis of predictive algorithms for collaborative filtering
J. Breese, D. Heckerman, and C. Kadie · 1998
Earlier work this paper cites.
On the momentum term in gradient descent learning algorithms
N. Qian · 1999
Earlier work this paper cites.
Laplacian eigenmaps and spectral techniques for embedding and clustering
M. Belkin and P. Niyogi · 2001
Earlier work this paper cites.
Hybrid recommender systems: survey and experiments
R. D. Burke · 2002
Earlier work this paper cites.
Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
Earlier work this paper cites.
A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
Earlier work this paper cites.
Content-based recommendation systems
M. Pazzani and D. Billsus · 2007
Earlier work this paper cites.
Exact matrix completion via convex optimization
E. J. Candès and B. Recht · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Y. Koren, R. Bell, and C. Volinsky · 2009
Earlier work this paper cites.
Relation regularized matrix factorization
W. Li and D. Yeung · 2009
Earlier work this paper cites.
A singular value thresholding algorithm for matrix completion
J. Cai, E. J. Candès, and Z. Shen · 2010
Earlier work this paper cites.
Graph regularized nonnegative matrix factorization for data representation
D. Cai, X. He, J. Han, and T. S. Huang · 2011
Earlier work this paper cites.
Wavelets on graphs via spectral graph theory
D. K. Hammond, P. Vandergheynst, and R. Gribonval · 2011
Earlier work this paper cites.
Recommender systems with social regularization
H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King · 2011
Earlier work this paper cites.
A simpler approach to matrix completion
B. Recht · 2011
Cited alongside, same era.
Unifying nuclear norm and bilinear factorization approaches for low-rank matrix decomposition
R. S. Cabral, F. Torre, J. P. Costeira, and A. Bernardino · 2013
Cited alongside, same era.
Provable inductive matrix completion
P. Jain and I. S. Dhillon · 2013
Cited alongside, same era.
The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains
D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst · 2013
Cited alongside, same era.
Speedup matrix completion with side information: application to multi-label learning
M. Xu, R. Jin, and Z. Zhou · 2013
Cited alongside, same era.
Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Later among the works it cites.
Inductive representation learning on large graphs
W. L. Hamilton, Z. Ying, and J. Leskovec · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
Later among the works it cites.
Geometric matrix completion with recurrent multi-graph neural networks
F. Monti, M. Bronstein, and X. Bresson · 2017
Later among the works it cites.
Automatic differentiation in PyTorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Later among the works it cites.
Stochastic training of graph convolutional networks with variance reduction
J. Chen, J. Zhu, and L. Song · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
Cited alongside, same era.
V. Kalofolias, X. Bresson, M. M. Bronstein, and P. Vandergheynst · 2014
Cited alongside, same era.
Deep convolutional networks on graph-structured data
M. Henaff, J. Bruna, and Y. LeCun · 2015
Cited alongside, same era.
Collaborative filtering with graph information: consistency and scalable methods
N. Rao, H. Yu, P. Ravikumar, and I. S. Dhillon · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
Cited alongside, same era.
Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2016
Cited alongside, same era.
Learning convolutional neural networks for graphs
M. Niepert, M. Ahmed, and K. Kutzkov · 2016
Cited alongside, same era.
Nonconvex optimization meets low-rank matrix factorization: an overview
Y. Chi, Y. M. Lu, and Y. Chen · 2018
Closest in time.
Large-scale learnable graph convolutional networks
H. Gao, Z. Wang, and S. Ji · 2018
Closest in time.
Global optimality in inductive matrix completion
M. Ghassemi, A. D. Sarwate, and N. Goela · 2018
Closest in time.
Adaptive sampling towards fast graph representation learning
W. Huang, T. Zhang, Y. Rong, and J. Huang · 2018
Closest in time.
Graph attention networks
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
Closest in time.
Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
Closest in time.
Global optimality in low-rank matrix optimization
Z. Zhu, Q. Li, G. Tang, and M. B. Wakin · 2018
Closest in time.
Mixhop: higher-order graph convolutional architectures via sparsified neighborhood mixing
S. Abu-El-Haija, B. Perozzi, A. Kapoor, H. Harutyunyan, N. Alipourfard, K. Lerman, G. V. Steeg, and A. Galstyan · 2019
Closest in time.
Predict then propagate: graph neural networks meet personalized PageRank
J. Klicpera, A. Bojchevski, and S. Gunnemann · 2019
Closest in time.