Fetching the paper…
Reading the bibliography…
We use deep learning to model interactions across two or more sets of objects, such as user-movie ratings, protein-drug bindings, or ternary user-item-tag interactions.
Introduction to statistical relational learning
Getoor, L. and Taskar, B · 2007
Earlier work this paper cites.
Restricted boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., and Hinton, G · 2007
Earlier work this paper cites.
Probabilistic matrix factorization
Mnih, A. and Salakhutdinov, R. R · 2008
Earlier work this paper cites.
Exact matrix completion via convex optimization
Candès, E. J. and Recht, B · 2009
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Koren, Y., Bell, R., and Volinsky, C · 2009
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
Earlier work this paper cites.
Local low-rank matrix approximation
Lee, J., Kim, S., Lebanon, G., and Singer, Y · 2013
Earlier work this paper cites.
Tensor decompositions for learning latent variable models
Anandkumar, A., Ge, R., Hsu, D., Kakade, S. M., and Telgarsky, M · 2014
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2014
Earlier work this paper cites.
Kalofolias, V., Bresson, X., Bronstein, M., and Vandergheynst, P · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Cited alongside, same era.
Neural network matrix factorization
Dziugaite, G. K. and Roy, D. M · 2015
Cited alongside, same era.
The movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2015
Cited alongside, same era.
Deep collaborative filtering via marginalized denoising auto-encoder
Li, S., Kawale, J., and Fu, Y · 2015
Cited alongside, same era.
Bayesian models of graphs, arrays and other exchangeable random structures
Orbanz, P. and Roy, D. M · 2015
Cited alongside, same era.
Collaborative filtering with graph information: Consistency and scalable methods
Deep learning for predicting human strategic behavior
Hartford, J. S., Wright, J. R., and Leyton-Brown, K · 2016
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
Later among the works it cites.
Statistical relational artificial intelligence: Logic, probability, and computation
Raedt, L. D., Kersting, K., Natarajan, S., and Poole, D · 2016
Later among the works it cites.
Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W. W., and Salakhutdinov, R · 2016
Later among the works it cites.
A neural autoregressive approach to collaborative filtering
Zheng, Y., Tang, B., Ding, W., and Zhou, H · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rao, N., Yu, H.-F., Ravikumar, P. K., and Dhillon, I. S · 2015
Cited alongside, same era.
Autorec: Autoencoders meet collaborative filtering
Sedhain, S., Menon, A. K., Sanner, S., and Xie, L · 2015
Cited alongside, same era.
Efficient object localization using convolutional networks
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., and Bregler, C · 2015
Cited alongside, same era.
Collaborative deep learning for recommender systems
Wang, H., Wang, N., and Yeung, D.-Y · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
Factorized variational autoencoders for modeling audience reactions to movies
Deng, Z., Navarathna, R., Carr, P., Mandt, S., Yue, Y., Matthews, I., and Mori, G
Cited in the paper.
Graph convolutional matrix completion
Berg, R. v. d., Kipf, T. N., and Welling, M · 2017
Later among the works it cites.
Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
Later among the works it cites.
Geometric matrix completion with recurrent multi-graph neural networks
Monti, F., Bronstein, M., and Bresson, X · 2017
Later among the works it cites.
Equivariance through parameter-sharing
Ravanbakhsh, S., Schneider, J., and Poczos, B · 2017
Later among the works it cites.
Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
Later among the works it cites.