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Collaborative Filtering (CF) signals are crucial for a Recommender System~(RS) model to learn user and item embeddings.
Applied linear algebra , volume 3
Noble, B., Daniel, J. W., et al · 1988
Earlier work this paper cites.
Bpr: Bayesian personalized ranking from implicit feedback
Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
Earlier work this paper cites.
Factorization machines
Rendle, S · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
The movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2015
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R. and McAuley, J · 2016
Earlier work this paper cites.
Modeling user exposure in recommendation
Liang, D., Charlin, L., McInerney, J., and Blei, D. M · 2016
Earlier work this paper cites.
Graph convolutional matrix completion
Berg, R. v. d., Kipf, T. N., and Welling, M · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
Hamilton, W. L., Ying, Z., and Leskovec, J · 2017
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Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Joint deep modeling of users and items using reviews for recommendation
Zheng, L., Noroozi, V., and Yu, P. S · 2017
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Representation learning on graphs with jumping knowledge networks
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., and Jegelka, S · 2018
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Hop-rec: high-order proximity for implicit recommendation
Yang, J.-H., Chen, C.-M., Wang, C.-J., and Tsai, M.-F · 2018
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Jscn: Joint spectral convolutional network for cross domain recommendation
Liu, Z., Zheng, L., Zhang, J., Han, J., and Yu, P. S · 2019
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Dropedge: Towards deep graph convolutional networks on node classification
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Later among the works it cites.
Neural graph collaborative filtering
Wang, X., He, X., Wang, M., Feng, F., and Chua, T · 2019
Later among the works it cites.
Simplifying graph convolutional networks
Wu, F., Jr., A. H. S., Zhang, T., Fifty, C., Yu, T., and Weinberger, K. Q · 2019
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Gresnet: Graph residual network for reviving deep gnns from suspended animation
Zhang, J. and Meng, L · 2019
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Spectral collaborative filtering
Zheng, L., Lu, C.-T., Jiang, F., Zhang, J., and Yu, P. S · 2018
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Layer-dependent importance sampling for training deep and large graph convolutional networks
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Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach
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