research-article

UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation

Authors:

Bo ZhengAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2078 - 2087

https://doi.org/10.1145/3485447.3512081

Published: 25 April 2022 Publication History

Abstract

In online advertising, conventional post-click conversion rate (CVR) estimation models are trained using clicked samples. However, during online serving the models need to estimate for all impression ads, leading to the sample selection bias (SSB) issue. Intuitively, providing reliable supervision signals for unclicked ads is a feasible way to alleviate the SSB issue. This paper proposes an uncertainty-regularized knowledge distillation (UKD) framework to debias CVR estimation via distilling knowledge from unclicked ads. A teacher model learns click-adaptive representations and produces pseudo-conversion labels on unclicked ads as supervision signals. Then a student model is trained on both clicked and unclicked ads with knowledge distillation, performing uncertainty modeling to alleviate the inherent noise in pseudo-labels. Experiments on billion-scale datasets show that UKD outperforms previous debiasing methods. Online results verify that UKD achieves significant improvements.

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Cited By

Ge LHu CMa GLiu JZhang HWooldridge MDy JNatarajan S(2024)Discrepancy and uncertainty aware denoising knowledge distillation for zero-shot cross-lingual named entity recognitionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i16.29762(18056-18064)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i16.29762
Cui YLiu FWang PWang BTang HWan YWang JChen J(2024)Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688118(507-517)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688118
Li KChen XLeng LXu JSun JRezaei B(2024)Privacy Preserving Conversion Modeling in Data Clean RoomProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688054(819-822)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688054
Show More Cited By

Index Terms

UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
1. Computing methodologies
2. Information systems
  1. Information systems applications
    1. Computational advertising
  2. World Wide Web
    1. Online advertising
      1. Display advertising

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Ge LHu CMa GLiu JZhang HWooldridge MDy JNatarajan S(2024)Discrepancy and uncertainty aware denoising knowledge distillation for zero-shot cross-lingual named entity recognitionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i16.29762(18056-18064)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i16.29762
Cui YLiu FWang PWang BTang HWan YWang JChen J(2024)Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language ModelsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688118(507-517)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688118
Li KChen XLeng LXu JSun JRezaei B(2024)Privacy Preserving Conversion Modeling in Data Clean RoomProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688054(819-822)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688054
Zhang KShi TDai SZhang XLi YLu JZang XSong YXu JSerra ESpezzano F(2024)SAQRec: Aligning Recommender Systems to User Satisfaction via Questionnaire FeedbackProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679643(3165-3175)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679643
Su HMeng LZhu LLu KLi JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)DDPO: Direct Dual Propensity Optimization for Post-Click Conversion Rate EstimationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657817(1179-1188)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657817
Zhang XHuang CZheng KSu HJi TWang WQi HLi JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Adversarial-Enhanced Causal Multi-Task Framework for Debiasing Post-Click Conversion Rate EstimationProceedings of the ACM Web Conference 202410.1145/3589334.3645379(3287-3296)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645379
Ge LHu CMa GZhang HLiu J(2024)Den-ML: Multi-source cross-lingual transfer via denoising mutual learningInformation Processing & Management10.1016/j.ipm.2024.10383461:6(103834)Online publication date: Nov-2024
https://doi.org/10.1016/j.ipm.2024.103834
Wei SYang ZZhang JZeng YLi QXiao Y(2024)Click-through conversion rate prediction model of book e-commerce platform based on feature combination and representationExpert Systems with Applications10.1016/j.eswa.2023.122276238(122276)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.122276
Zhao YYan XGui XHan SSheng XYu GChen JXu ZZheng BFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615475(4981-4987)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615475
Dai SZhou YXu JWen JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Dually Enhanced Delayed Feedback Modeling for Streaming Conversion Rate PredictionProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614856(390-399)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614856
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