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In online advertising under the cost-per-conversion (CPA) model, accurate conversion rate (CVR) prediction is crucial.
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
Li, H.; Pan, F.; Ao, X.; Yang, Z.; Lu, M.; Pan, J.; Liu, D.; Xiao, L.; and He, Q. 2021 · 1919
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Influence Function for Unbiased Recommendation
Yu, J.; Zhu, H.; Chang, C.; Feng, X.; Yuan, B.; He, X.; and Dong, Z. 2020 · 1932
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A stochastic approximation method
Robbins, H.; and Monro, S. 1951 · 1951
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The influence curve and its role in robust estimation
Hampel, F. R. 1974 · 1974
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Adaptive subgradient methods for online learning and stochastic optimization
Duchi, J.; Hazan, E.; and Singer, Y. 2011 · 2011
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Estimating conversion rate in display advertising from past erformance data
Lee, K.; Orten, B.; Dasdan, A.; and Li, W. 2012 · 2012
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Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
Johnson, R.; and Zhang, T. 2013 · 2013
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Modeling delayed feedback in display advertising
Chapelle, O. 2014 · 2014
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
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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
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Guo, H.; Tang, R.; Ye, Y.; Li, Z.; and He, X. 2017 · 2017
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Understanding Black-box Predictions via Influence Functions
Koh, P. W.; and Liang, P. 2017 · 2017
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Model ensemble for click prediction in bing search ads
Ling, X.; Deng, W.; Gu, C.; Zhou, H.; Li, C.; and Sun, F. 2017 · 2017
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A Practical Framework of Conversion Rate Prediction for Online Display Advertising
Lu, Q.; Pan, S.; Wang, L.; Pan, J.; Wan, F.; and Yang, H. 2017 · 2017
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Deep & cross network for ad click predictions
Wang, R.; Fu, B.; Fu, G.; and Wang, M. 2017 · 2017
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Explaining latent factor models for recommendation with influence functions
Cheng, W.; Shen, Y.; Zhu, Y.; and Huang, L. 2018 · 2018
Cited alongside, same era.
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.
A Nonparametric Delayed Feedback Model for Conversion Rate Prediction
Yoshikawa, Y.; and Imai, Y. 2018 · 2018
Cited alongside, same era.
Data Cleansing for Models Trained with SGD
Hara, S.; Nitanda, A.; and Maehara, T. 2019 · 2019
Cited alongside, same era.
On the Accuracy of Influence Functions for Measuring Group Effects
Koh, P. W.; Ang, K.; Teo, H. H. K.; and Liang, P. 2019 · 2019
Cited alongside, same era.
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
Yang, J.; Li, X.; Han, S.; Zhuang, T.; Zhan, D.; Zeng, X.; and Tong, B. 2021 · 2021
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Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction
Chen, Y.; Jin, J.; Zhao, H.; Wang, P.; Liu, G.; Xu, J.; and Zheng, B. 2022 · 2022
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Calibrated Conversion Rate Prediction via Knowledge Distillation under Delayed Feedback in Online Advertising
Guo, Y.; Li, H.; Ao, X.; Lu, M.; Liu, D.; Xiao, L.; Jiang, J.; and He, Q. 2022 · 2022
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Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Li, P.; and Liu, H. 2022 · 2022
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Learning Classifiers under Delayed Feedback with a Time Window Assumption
Yasui, S.; and Kato, M. 2022 · 2022
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Addressing delayed feedback for continuous training with neural networks in CTR prediction
Ktena, S. I.; Tejani, A.; Theis, L.; Myana, P. K.; Dilipkumar, D.; Huszár, F.; Yoo, S.; and Shi, W. 2019 · 2019
Cited alongside, same era.
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Song, W.; Shi, C.; Xiao, Z.; Duan, Z.; Xu, Y.; Zhang, M.; and Tang, J. 2019 · 2019
Cited alongside, same era.
On Second-Order Group Influence Functions for Black-Box Predictions
Basu, S.; You, X.; and Feizi, S. 2020 · 2020
Cited alongside, same era.
Estimating Training Data Influence by Tracing Gradient Descent
Pruthi, G.; Liu, F.; Kale, S.; and Sundararajan, M. 2020 · 2020
Cited alongside, same era.
Dual Learning Algorithm for Delayed Conversions
Saito, Y.; Morishita, G.; and Yasui, S. 2020 · 2020
Cited alongside, same era.
A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
Yasui, S.; Morishita, G.; Fujita, K.; and Shibata, M. 2020 · 2020
Cited alongside, same era.
Influence Functions in Deep Learning Are Fragile
Basu, S.; Pope, P.; and Feizi, S. 2021 · 2021
Cited alongside, same era.
Chen, R.; Yang, J.; Xiong, H.; Bai, J.; Hu, T.; Hao, J.; Feng, Y.; Zhou, J. T.; Wu, J.; and Liu, Z. 2023 · 2023
Later among the works it cites.
Dually Enhanced Delayed Feedback Modeling for Streaming Conversion Rate Prediction
Dai, S.; Zhou, Y.; Xu, J.; and Wen, J. 2023 · 2023
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Selective and collaborative influence function for efficient recommendation unlearning
Li, Y.; Chen, C.; Zheng, X.; Zhang, Y.; Gong, B.; Wang, J.; and Chen, L. 2023 · 2023
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MUter: Machine Unlearning on Adversarially Trained Models
Liu, J.; Xue, M.; Lou, J.; Zhang, X.; Xiong, L.; and Qin, Z. 2023 · 2023
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Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
Wang, Y.; Sun, P.; Zhang, M.; Jia, Q.; Li, J.; and Ma, S. 2023 · 2023
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GIF: A General Graph Unlearning Strategy via Influence Function
Wu, J.; Yang, Y.; Qian, Y.; Sui, Y.; Wang, X.; and He, X. 2023 · 2023
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Recommendation Unlearning via Influence Function
Zhang, Y.; Hu, Z.; Bai, Y.; Feng, F.; Wu, J.; Wang, Q.; and He, X. 2023 · 2023
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”What Data Benefits My Classifier?” Enhancing Model Performance and Interpretability through Influence-Based Data Selection
Chhabra, A.; Li, P.; Mohapatra, P.; and Liu, H. 2024 · 2024
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A counterfactual explanation method based on modified group influence function for recommendation
Guo, Y.; Cai, F.; Pan, Z.; Shao, T.; Chen, H.; and Zhang, X. 2024 · 2024
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FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
Wang, H.; Wu, Z.; and He, J. 2024 · 2024
Later among the works it cites.