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Improving Ad Click Prediction by Considering Non-displayed Events

Authors:

Meng-Yuan Yang,

Chih-Yao Chang,

Chih-Jen LinAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 329 - 338

https://doi.org/10.1145/3357384.3358058

Published: 03 November 2019 Publication History

Abstract

Click-through rate (CTR) prediction is the core problem of building advertising systems. Most existing state-of-the-art approaches model CTR prediction as binary classification problems, where displayed events with and without click feedbacks are respectively considered as positive and negative instances for training and offline validation. However, due to the selection mechanism applied in most advertising systems, a selection bias exists between distributions of displayed and non-displayed events. Conventional CTR models ignoring the bias may have inaccurate predictions and cause a loss of the revenue. To alleviate the bias, we need to conduct counterfactual learning by considering not only displayed events but also non-displayed events. In this paper, through a review of existing approaches of counterfactual learning, we point out some difficulties for applying these approaches for CTR prediction in a real-world advertising system. To overcome these difficulties, we propose a novel framework for counterfactual CTR prediction. In experiments, we compare our proposed framework against state-of-the-art conventional CTR models and existing counterfactual learning approaches. Experimental results show significant improvements.

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Cited By

El Alaoui DRiffi JSabri AAghoutane BYahyaouy ATairi H(2024)Comparative Study of Filtering Methods for Scientific Research Article RecommendationsBig Data and Cognitive Computing10.3390/bdcc81201908:12(190)Online publication date: 16-Dec-2024
https://doi.org/10.3390/bdcc8120190
Lin JLi QXie GGuan ZJiang YXu TZhang ZZhao PCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Mitigating Sample Selection Bias with Robust Domain Adaption in Multimedia RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680615(7581-7590)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680615
Jian MBai YFu XGuo JShi GWu L(2024)Counterfactual Graph Convolutional Learning for Personalized RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365563215:4(1-20)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1145/3655632
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Index Terms

Improving Ad Click Prediction by Considering Non-displayed Events
1. Information systems
  1. Information systems applications
    1. Computational advertising

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Published In

cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 ACM.

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Published: 03 November 2019

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November 3 - 7, 2019

Beijing, China

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Cited By

El Alaoui DRiffi JSabri AAghoutane BYahyaouy ATairi H(2024)Comparative Study of Filtering Methods for Scientific Research Article RecommendationsBig Data and Cognitive Computing10.3390/bdcc81201908:12(190)Online publication date: 16-Dec-2024
https://doi.org/10.3390/bdcc8120190
Lin JLi QXie GGuan ZJiang YXu TZhang ZZhao PCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Mitigating Sample Selection Bias with Robust Domain Adaption in Multimedia RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680615(7581-7590)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680615
Jian MBai YFu XGuo JShi GWu L(2024)Counterfactual Graph Convolutional Learning for Personalized RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365563215:4(1-20)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1145/3655632
Zhang FXu YChen HYuan XLiu QJiang YSerra ESpezzano F(2024)Effective Utilization of Large-scale Unobserved Data in Recommendation SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680067(5070-5077)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680067
Zheng YQin JWei PChen ZLin L(2024)CIPL: Counterfactual Interactive Policy Learning to Eliminate Popularity Bias for Online RecommendationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.329992935:12(17123-17136)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3299929
Li WYao DGong CChu XJing QZhou XZhang YFan YBi J(2024)CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00341(4477-4490)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00341
Zhang HWang SLi HZheng CChen XLiu LLuo SWu P(2024)Uncovering the Propensity Identification Problem in Debiased Recommendations2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00056(653-666)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00056
Chen XFeng SYang MZhao KXu RCui CChen M(2024)Modeling question difficulty for unbiased cognitive diagnosis: A causal perspectiveKnowledge-Based Systems10.1016/j.knosys.2024.111750294(111750)Online publication date: Jun-2024
https://doi.org/10.1016/j.knosys.2024.111750
Yang XLi XLiu ZWang YLu SLiu F(2024)Disentangled causal representation learning for debiasing recommendation with uniform dataApplied Intelligence10.1007/s10489-024-05497-954:8(6760-6775)Online publication date: 24-May-2024
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Liu NMeng SLiu XLi QJiang Y(2024)Disentangled Causal Embedding with Unbiased Knowledge Distillation for RecommendationAdvanced Data Mining and Applications10.1007/978-981-96-0850-8_9(128-142)Online publication date: 24-Dec-2024
https://doi.org/10.1007/978-981-96-0850-8_9
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