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Matrix completion models are among the most common formulations of recommender systems.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J., Heckerman, D., and Kadie, C · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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MovieLens unplugged: experiences with an occasionally connected recommender system
Miller, B. N. et al · 2003
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Maximum-Margin Matrix Factorization
Srebro, N., Rennie, J., and Jaakkola, T · 2004
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A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
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Content-based Recommendation Systems
Pazzani, M. and Billsus, D · 2007
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Exact Matrix Completion via Convex Optimization
Candès, E. and Recht, B · 2009
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Matrix factorization techniques for recommender systems
Koren, Y., Bell, R., and Volinsky, C · 2009
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
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A matrix factorization technique with trust propagation for recommendation in social networks
Jamali, M. and Ester, M · 2010
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Recommender systems with social regularization
Ma, H., Zhou, D., Liu, C., Lyu, M., and King, I · 2011
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Exact matrix completion via convex optimization
Candes, E. and Recht, B · 2012
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The Yahoo! music dataset and KDD-Cup’11
Dror, G., Koenigstein, N., Koren, Y., and Weimer, M · 2012
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Primal-dual algorithms for non-negative matrix factorization with the kullback-leibler divergence
Yanez, F. and Bach, F · 2012
Cited alongside, same era.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2013
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Provable inductive matrix completion
Jain, P. and Dhillon, I. S · 2013
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2015
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Geodesic convolutional neural networks on Riemannian manifolds
Masci, J., Boscaini, D., Bronstein, M. M., and Vandergheynst, P · 2015
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Collaborative filtering with graph information: Consistency and scalable methods
Rao, N., Yu, H.-F., Ravikumar, P. K., and Dhillon, I. S · 2015
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Song recommendation with non-negative matrix factorization and graph total variation
Benzi, K., Kalofolias, V., Bresson, X., and Vandergheynst, P · 2016
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Geometric deep learning: going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2016
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Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., and Vandergheynst, P · 2013
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Speedup matrix completion with side information: Application to multi-label learning
Xu, M., Jin, R, and Zhou, Z.-H · 2013
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Kalofolias, V., Bresson, X., Bronstein, M. M., and Vandergheynst, P · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks
Boscaini, D., Masci, J., Melzi, S., Bronstein, M. M., Castellani, U., and Vandergheynst, P · 2015
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K. et al · 2015
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Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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A harmonic extension approach for collaborative ranking
Kuang, D., Shi, Z., Osher, S., and Bertozzi, A · 2016
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Structured sequence modeling with graph convolutional recurrent networks
Seo, Y., Defferrard, M., Vandergheynst, P., and Bresson, X · 2016
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Learning multiagent communication with backpropagation
Sukhbaatar, S., Szlam, A., and Fergus, R · 2016
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Geometric deep learning on graphs and manifolds using mixture model CNNs
Monti, F., Boscaini, D., Masci, J., Rodolà, E., Svoboda, J., and Bronstein, M. M · 2017
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