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Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014 · 1958
Earlier work this paper cites.
Generalized linear models
Nelder, J. A.; and Wedderburn, R. W. 1972 · 1972
Earlier work this paper cites.
Predicting clicks: estimating the click-through rate for new ads
Richardson, M.; Dominowska, E.; and Ragno, R. 2007 · 2007
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
Earlier work this paper cites.
Coupled group lasso for web-scale ctr prediction in display advertising
Yan, L.; Li, W.-J.; Xue, G.-R.; and Han, D. 2014 · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
Earlier work this paper cites.
Wide & deep learning for recommender systems
Cheng, H.-T.; Koc, L.; Harmsen, J.; Shaked, T.; Chandra, T.; Aradhye, H.; Anderson, G.; Corrado, G.; Chai, W.; Ispir, M.; et al. 2016 · 2016
Earlier work this paper cites.
Deep neural networks for youtube recommendations
Covington, P.; Adams, J.; and Sargin, E. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Guo, H.; Tang, R.; Ye, Y.; Li, Z.; and He, X. 2017 · 2017
Earlier work this paper cites.
Deep & cross network for ad click predictions
Wang, R.; Fu, B.; Fu, G.; and Wang, M. 2017 · 2017
Earlier work this paper cites.
Entire space multi-task model: An effective approach for estimating post-click conversion rate
Ma, X.; Zhao, L.; Huang, G.; Wang, Z.; Hu, Z.; Zhu, X.; and Gai, K. 2018 · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
Cited alongside, same era.
FiBiNET: combining feature importance and bilinear feature interaction for click-through rate prediction
Huang, T.; Zhang, Z.; and Zhang, J. 2019 · 2019
Cited alongside, same era.
Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations
Tang, H.; Liu, J.; Zhao, M.; and Gong, X. 2020 · 2020
Cited alongside, same era.
Ensembled CTR prediction via knowledge distillation
Zhu, J.; Liu, J.; Li, W.; Lai, J.; He, X.; Chen, L.; and Zheng, Z. 2020 · 2020
Cited alongside, same era.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Gao, C.; Li, S.; Zhang, Y.; Chen, J.; Li, B.; Lei, W.; Jiang, P.; and He, X. 2022 · 2022
Later among the works it cites.
Adaptive Domain Interest Network for Multi-domain Recommendation
Jiang, Y.; Li, Q.; Zhu, H.; Yu, J.; Li, J.; Xu, Z.; Dong, H.; and Zheng, B. 2022 · 2022
Later among the works it cites.
CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation
Wang, Y.; Guo, H.; Chen, B.; Liu, W.; Liu, Z.; Zhang, Q.; He, Z.; Zheng, H.; Yao, W.; Zhang, M.; et al. 2022 · 2022
Later among the works it cites.
A Recommendation Algorithm Based on a Self-supervised Learning Pretrain Transformer
Xu, Y.-H.; Wang, Z.-H.; Wang, Z.-R.; Fan, R.; and Wang, X. 2022 · 2022
Later among the works it cites.
Apg: Adaptive parameter generation network for click-through rate prediction
Yan, B.; Wang, P.; Zhang, K.; Li, F.; Deng, H.; Xu, J.; and Zheng, B. 2022 · 2022
Later among the works it cites.
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Guo, H.; Chen, B.; Tang, R.; Zhang, W.; Li, Z.; and He, X. 2021 · 2021
Cited alongside, same era.
SAR-Net: A scenario-aware ranking network for personalized fair recommendation in hundreds of travel scenarios
Shen, Q.; Tao, W.; Zhang, J.; Wen, H.; Chen, Z.; and Lu, Q. 2021 · 2021
Cited alongside, same era.
One model to serve all: Star topology adaptive recommender for multi-domain ctr prediction
Sheng, X.-R.; Zhao, L.; Zhou, G.; Ding, X.; Dai, B.; Luo, Q.; Yang, S.; Lv, J.; Zhang, C.; Deng, H.; et al. 2021 · 2021
Cited alongside, same era.
Online behavioral advertising: An integrative review
Varnali, K. 2021 · 2021
Cited alongside, same era.
Deep learning for click-through rate estimation
Zhang, W.; Qin, J.; Guo, W.; Tang, R.; and He, X. 2021 · 2021
Cited alongside, same era.
CAN: feature co-action network for click-through rate prediction
Bian, W.; Wu, K.; Ren, L.; Pi, Q.; Zhang, Y.; Xiao, C.; Sheng, X.-R.; Zhu, Y.-N.; Chan, Z.; Mou, N.; et al. 2022 · 2022
Cited alongside, same era.
AdaSparse: Learning Adaptively Sparse Structures for Multi-Domain Click-Through Rate Prediction
Yang, X.; Peng, X.; Wei, P.; Liu, S.; Wang, L.; and Zheng, B. 2022 · 2022
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Click-through rate prediction in online advertising: A literature review
Yang, Y.; and Zhai, P. 2022 · 2022
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A survey on cross-domain recommendation: taxonomies, methods, and future directions
Zang, T.; Zhu, Y.; Liu, H.; Zhang, R.; and Yu, J. 2022 · 2022
Later among the works it cites.
Leaving no one behind: A multi-scenario multi-task meta learning approach for advertiser modeling
Zhang, Q.; Liao, X.; Liu, Q.; Xu, J.; and Zheng, B. 2022 · 2022
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AutoTransfer: Instance Transfer for Cross-Domain Recommendations
Gao, J.; Zhao, X.; Chen, B.; Yan, F.; Guo, H.; and Tang, R. 2023 · 2023
Closest in time.
DFFM: Domain Facilitated Feature Modeling for CTR Prediction
Guo, W.; Zhu, C.; Yan, F.; Chen, B.; Liu, W.; Guo, H.; Zheng, H.; Liu, Y.; and Tang, R. 2023 · 2023
Closest in time.