Fetching the paper…
Reading the bibliography…
Advertising and feed ranking are essential to many Internet companies such as Facebook.
Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, 1930–1939
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018 · 1939
Earlier work this paper cites.
Learning to forget: Continual prediction with LSTM
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. 1999 · 1999
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics . 249–256
Xavier Glorot and Yoshua Bengio. 2010 · 2010
Earlier work this paper cites.
Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. Omnipress
Thore Graepel, Joaquin Quinonero Candela, Thomas Borchert, and Ralf Herbrich. 2010 · 2010
Earlier work this paper cites.
Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur. 2010 · 2010
Earlier work this paper cites.
Factorization machines. In Data Mining (ICDM), 2010 IEEE 10th International Conference on . IEEE, 995–1000
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems . 1097–1105
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Factorization machines with libfm
Steffen Rendle. 2012 · 2012
Earlier work this paper cites.
Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 1222–1230
H Brendan McMahan, Gary Holt, David Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, et al · 2013
Cited alongside, same era.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising . ACM, 1–9
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, et al · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Deep learning over multi-field categorical data. In European conference on information retrieval . Springer, 45–57
Weinan Zhang, Tianming Du, and Jun Wang. 2016 · 2016
Later among the works it cites.
Language modeling with gated convolutional networks. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 933–941
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017 · 2017
Later among the works it cites.
Convolutional sequence to sequence learning. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 1243–1252
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
Later among the works it cites.
Deepfm: a factorization-machine based neural network for ctr prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Later among the works it cites.
Deep & cross network for ad click predictions. In Proceedings of the ADKDD’17 . ACM, 12
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015 · 2015
Cited alongside, same era.
Wide & deep learning for recommender systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems . ACM, 7–10
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, et al · 2016
Cited alongside, same era.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . ACM, 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Cited alongside, same era.
Field-aware factorization machines for CTR prediction. In Proceedings of the 10th ACM Conference on Recommender Systems . ACM, 43–50
Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016 · 2016
Cited alongside, same era.
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
Later among the works it cites.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018 · 2018
Later among the works it cites.
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. In Proceedings of the 13th ACM Conference on RecSys 2019 . 169–177
Tongwen Huang, Zhiqi Zhang, and Junlin Zhang. 2019 · 2019
Later among the works it cites.
Hierarchical Gating Networks for Sequential Recommendation
Chen Ma, Peng Kang, and Xue Liu. 2019 · 2019
Later among the works it cites.