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Click-Through Rate (CTR) prediction is critical for industrial recommender systems, where most deep CTR models follow an Embedding \& Feature Interaction paradigm.
Neural Input Search for Large Scale Recommendation Models
Manas R. Joglekar, Cong Li, Jay K. Adams, Pranav Khaitan, and Quoc V. Le. 2019 · 1907
Earlier work this paper cites.
Feature Selection with Decision Tree Criterion. In HIS . IEEE Computer Society, 212–217
Krzysztof Grabczewski and Norbert Jankowski. 2005 · 2005
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Factorization Machines. In Proc. IEEE Int. Conf. Data Mining
Steffen Rendle. 2010 · 2010
Earlier work this paper cites.
Ad click prediction: a view from the trenches. In SIGKDD . 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
Earlier work this paper cites.
Practical Lessons from Predicting Clicks on Ads at Facebook. In ADKDD . ACM
Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, and Joaquin Quiñonero Candela. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In ICCV . 1026–1034
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015 · 2015
Earlier work this paper cites.
Wide & Deep Learning for Recommender Systems. In Proc. Workshop Deep Learning for Recommender Systems
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016 · 2016
Earlier work this paper cites.
Deep Neural Networks for YouTube Recommendations. In RecSys
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Field-aware Factorization Machines for CTR Prediction. In RecSys
Yu-Chin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. 2016 · 2016
Earlier work this paper cites.
Product-based neural networks for user response prediction. In ICDM . IEEE, 1149–1154
Yanru Qu, Han Cai, Kan Ren, Weinan Zhang, Yong Yu, Ying Wen, and Jun Wang. 2016 · 2016
Cited alongside, same era.
Deep learning over multi-field categorical data. In ECIR . Springer, 45–57
Weinan Zhang, Tianming Du, and Jun Wang. 2016 · 2016
Cited alongside, same era.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction. In IJCAI
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Cited alongside, same era.
Deep & Cross Network for Ad Click Predictions. In ADKDD . ACM, 12:1–12:7
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
Cited alongside, same era.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems. In SIGKDD
Jianxun Lian, Xiaohuan Zhou, Fuzheng Zhang, Zhongxia Chen, Xing Xie, and Guangzhong Sun. 2018 · 2018
Cited alongside, same era.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
Maxim Naumov, Dheevatsa Mudigere, Hao-Jun Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson G. Azzolini, Dmytro Dzhulgakov, Andrey Mallevich, Ilia Cherniavskii, Yinghai Lu, Raghuraman Krishnamoorthi, Ansha Yu, Volodymyr Kondratenko, Stephanie Pereira, Xianjie Chen, Wenlin Chen, Vijay Rao, Bill Jia, Liang Xiong, and Misha Smelyanskiy. 2019 · 2019
Later among the works it cites.
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
Yanru Qu, Bohui Fang, Weinan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, and Xiuqiang He. 2019 · 2019
Later among the works it cites.
Autoint: Automatic feature interaction learning via self-attentive neural networks. In CIKM . 1161–1170
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019 · 2019
Later among the works it cites.
Deep Interest Evolution Network for Click-Through Rate Prediction. In AAAI . 5941–5948
Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019 · 2019
Later among the works it cites.
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Deep Interest Network for Click-Through Rate Prediction. In SIGKDD . 1059–1068
Guorui Zhou, Xiaoqiang Zhu, Chengru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
Cited alongside, same era.
Mixed dimension embeddings with application to memory-efficient recommendation systems
Antonio Ginart, Maxim Naumov, Dheevatsa Mudigere, Jiyan Yang, and James Zou. 2019 · 2019
Cited alongside, same era.
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction. In RecSys . ACM, 169–177
Tongwen Huang, Zhiqi Zhang, and Junlin Zhang. 2019 · 2019
Cited alongside, same era.
DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks. In SIGKDD . ACM, 384–394
Guolin Ke, Zhenhui Xu, Jia Zhang, Jiang Bian, and Tie-Yan Liu. 2019 · 2019
Cited alongside, same era.
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction. In WWW
Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, and Yuzhou Zhang. 2019 · 2019
Cited alongside, same era.
AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction. In SIGIR . ACM
Bin Liu, Niannan Xue, Huifeng Guo, Ruiming Tang, Stefanos Zafeiriou, Xiuqiang He, and Zhenguo Li. 2020a
Cited in the paper.
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction. In SIGKDD
Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, and Yong Yu. 2020b
Cited in the paper.
MindSpore
2020 · 2020
Closest in time.
Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems. In CIKM . ACM, 2493–2500
Yulong Gu, Zhuoye Ding, Shuaiqiang Wang, Lixin Zou, Yiding Liu, and Dawei Yin. 2020 · 2020
Closest in time.
Deep Hash Embedding for Large-Vocab Categorical Feature Representations
Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, and Ed H. Chi. 2020 · 2020
Closest in time.
User Behavior Retrieval for Click-Through Rate Prediction. In SIGIR . ACM
Jiarui Qin, Weinan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, and Yong Yu. 2020 · 2020
Closest in time.
A Practical Incremental Method to Train Deep CTR Models
Yichao Wang, Huifeng Guo, Ruiming Tang, Zhirong Liu, and Xiuqiang He. 2020 · 2020
Closest in time.
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen, Xudong Zheng, Xiaobing Liu, and Jiliang Tang. 2020 · 2020
Closest in time.