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User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system.
Dropout: a simple way to prevent neural networks from overfitting.,
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, · 1904
Earlier work this paper cites.
Factorization machines,
S. Rendle, · 2010
Earlier work this paper cites.
Pairwise interaction tensor factorization for personalized tag recommendation,
S. Rendle, L. Schmidt-Thieme, · 2010
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines,
V. Nair, G. E. Hinton, · 2010
Earlier work this paper cites.
Ad click prediction: a view from the trenches,
H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, et al., · 2013
Earlier work this paper cites.
Criteo, Kaggle display advertising challenge dataset, http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/ , 2014
2014
Earlier work this paper cites.
Adam: A method for stochastic optimization,
D. Kingma, J. Ba, · 2014
Earlier work this paper cites.
A convolutional click prediction model,
Q. Liu, F. Yu, S. Wu, L. Wang, · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift,
S. Ioffe, C. Szegedy, · 2015
Cited alongside, same era.
Sensembed: Learning sense embeddings for word and relational similarity.,
I. Iacobacci, M. T. Pilehvar, R. Navigli, · 2015
Cited alongside, same era.
Do multi-sense embeddings improve natural language understanding?,
J. Li, D. Jurafsky, · 2015
Cited alongside, same era.
Field-aware factorization machines for ctr prediction,
Y. Juan, Y. Zhuang, W.-S. Chin, C.-J. Lin, · 2016
Cited alongside, same era.
Product-based neural networks for user response prediction,
Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen, J. Wang, · 2016
Deep learning over multi-field categorical data,
W. Zhang, T. Du, J. Wang, · 2016
Later among the works it cites.
Deep & cross network for ad click predictions (2017) 1–7
R. Wang, B. Fu, G. Fu, M. Wang, · 2017
Later among the works it cites.
Deepfm: A factorization-machine based neural network for ctr prediction,
H. Guo, R. Tang, Y. Ye, Z. Li, X. He, · 2017
Later among the works it cites.
J. Xiao, H. Ye, X. He, H. Zhang, F. Wu, T.-S. Chua, · 2017
Later among the works it cites.
Deep interest network for click-through rate prediction,
G. Zhou, X. Zhu, C. Song, Y. Fan, H. Zhu, X. Ma, Y. Yan, J. Jin, H. Li, K. Gai, · 2018
Later among the works it cites.
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Cited alongside, same era.
Wide & deep learning for recommender systems,
H.-T. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, et al., · 2016
Cited alongside, same era.
Tencent, Tencent ads algorithm competition, https://algo.qq.com/home/home/index.html , 2018
2018
Later among the works it cites.