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Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based Approach

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Xiaotie DengAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 1291 - 1302

https://doi.org/10.1145/3580305.3599403

Published: 04 August 2023 Publication History

Abstract

Learning-based ad auctions have increasingly been adopted in online advertising. However, existing approaches neglect externalities, such as the interaction between ads and organic items. In this paper, we propose a general framework, namely Score-Weighted VCG, for designing learning-based ad auctions that account for externalities. The framework decomposes the optimal auction design into two parts: designing a monotone score function and an allocation algorithm, which facilitates data-driven implementation. Theoretical results demonstrate that this framework produces the optimal incentive-compatible and individually rational ad auction under various externality-aware CTR models while being data-efficient and robust. Moreover, we present an approach to implement the proposed framework with a matching-based allocation algorithm. Experiment results on both real-world and synthetic data illustrate the effectiveness of the proposed approach.

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References

[1]

Yang Cai, Constantinos Daskalakis, and S. Matthew Weinberg. 2012. Optimal Multi-dimensional Mechanism Design: Reducing Revenue to Welfare Maximization. 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science (2012), 130--139.

Digital Library

[2]

Ruggiero Cavallo, Maxim Sviridenko, and Christopher A. Wilkens. 2018. Matching Auctions for Search and Native Ads. Proceedings of the 2018 ACM Conference on Economics and Computation (2018).

Digital Library

[3]

Chi Chen, Hui Chen, Kangzhi Zhao, Junsheng Zhou, Li He, Hongbo Deng, Jian Xu, Bo Zheng, Yong Zhang, and Chunxiao Xing. 2022. EXTR: Click-Through Rate Prediction with Externalities in E-Commerce Sponsored Search. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2732--2740.

Digital Library

[4]

Michael J Curry, Uro Lyi, Tom Goldstein, and John P Dickerson. 2022. Learning revenue-maximizing auctions with differentiable matching. In International Conference on Artificial Intelligence and Statistics. PMLR, 6062--6073.

[5]

Xiaotie Deng, Yang Sun, Ming Yin, and Yunhong Zhou. 2010. Mechanism design for multi-slot ads auction in sponsored search markets. In Frontiers in Algorithmics: 4th International Workshop, FAW 2010, Wuhan, China, August 11-13, 2010. Proceedings 4. Springer, 11--22.

[6]

Zhijian Duan, Haoran Sun, Yurong Chen, and Xiaotie Deng. 2023. A Scalable Neural Network for DSIC Affine Maximizer Auction Design. arXiv preprint arXiv:2305.12162 (2023).

[7]

Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, and Xiaotie Deng. 2022. A context-integrated transformer-based neural network for auction design. In International Conference on Machine Learning. PMLR, 5609--5626.

[8]

Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David Parkes, and Sai Srivatsa Ravindranath. 2019. Optimal auctions through deep learning. In International Conference on Machine Learning. PMLR, 1706--1715.

[9]

Benjamin Edelman, Michael Ostrovsky, and Michael Schwarz. 2007. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American economic review, Vol. 97, 1 (2007), 242--259.

[10]

Nicola Gatti, Alessandro Lazaric, and Francesco Trovò. 2012. A truthful learning mechanism for contextual multi-slot sponsored search auctions with externalities. In Proceedings of the 13th ACM Conference on Electronic Commerce. 605--622.

Digital Library

[11]

Shivam Gupta. 2016. On a modification of the VCG mechanism and its optimality. Oper. Res. Lett., Vol. 44 (2016), 415--418.

Digital Library

[12]

Dmitry Ivanov, Iskander Safiulin, Igor Filippov, and Ksenia Balabaeva. 2022. Optimal-er Auctions through Attention. In Advances in Neural Information Processing Systems.

[13]

Bernard J Jansen and Tracy Mullen. 2008. Sponsored search: an overview of the concept, history, and technology. International Journal of Electronic Business, Vol. 6, 2 (2008), 114--131.

[14]

Przemyslaw Jeziorski and Ilya Segal. 2015. What makes them click: Empirical analysis of consumer demand for search advertising. American Economic Journal: Microeconomics, Vol. 7, 3 (2015), 24--53.

[15]

J. Kiefer. 1957. Optimum Sequential Search and Approximation Methods Under Minimum Regularity Assumptions. Journal of The Society for Industrial and Applied Mathematics, Vol. 5 (1957), 105--136.

[16]

Roger W. Koenker and Gilbert W. Jr. Bassett. 2007. Regression Quantiles.

[17]

Harold W. Kuhn. 1955. The Hungarian method for the assignment problem. Naval Research Logistics (NRL), Vol. 52 (1955).

[18]

Guogang Liao, Xuejian Li, Ze Wang, Fan Yang, Muzhi Guan, Bingqi Zhu, Yongkang Wang, Xingxing Wang, and Dong Wang. 2022. NMA: Neural Multi-slot Auctions with Externalities for Online Advertising. arXiv preprint arXiv:2205.10018 (2022).

[19]

Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, Yiqing Wang, Dagui Chen, Jian Xu, et al. 2021. Neural auction: End-to-end learning of auction mechanisms for e-commerce advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3354--3364.

Digital Library

[20]

Roger B Myerson. 1981. Optimal auction design. Mathematics of operations research, Vol. 6, 1 (1981), 58--73.

[21]

Jad Rahme, Samy Jelassi, and S. Matthew Weinberg. 2021. Auction Learning as a Two-Player Game. In International Conference on Learning Representations.

[22]

Tuomas Sandholm and Anton Likhodedov. 2015. Automated design of revenue-maximizing combinatorial auctions. Operations Research, Vol. 63, 5 (2015), 1000--1025.

Digital Library

[23]

Shai Shalev-Shwartz and Shai Ben-David. 2014. Understanding machine learning: From theory to algorithms. Cambridge university press.

Digital Library

[24]

Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019. Autoint: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 1161--1170.

Digital Library

[25]

Zhulin Tao, Xiang Wang, Xiangnan He, Xianglin Huang, and Tat-Seng Chua. 2020. HoAFM: a high-order attentive factorization machine for CTR prediction. Information Processing & Management, Vol. 57, 6 (2020), 102076.

[26]

Yiqing Wang, Junqi Jin, Zhenzhe Zheng, Haiyang Xu, Fan Wu, Yuning Jiang, and Guihai Chen. 2021. Multi-objective Dynamic Auction Mechanism for Online Advertising. In 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 1--8.

[27]

Yiqing Wang, Xiangyu Liu, Zhenzhe Zheng, Zhilin Zhang, Miao Xu, Chuan Yu, and Fan Wu. 2022. On Designing a Two-stage Auction for Online Advertising. In Proceedings of the ACM Web Conference 2022. 90--99.

Digital Library

[28]

Chao Wen, Miao Xu, Zhilin Zhang, Zhenzhe Zheng, Yuhui Wang, Xiangyu Liu, Yu Rong, Dong Xie, Xiaoyang Tan, Chuan Yu, et al. 2022. A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 1129--1139.

Digital Library

[29]

Zhilin Zhang, Xiangyu Liu, Zhenzhe Zheng, Chenrui Zhang, Miao Xu, Junwei Pan, Chuan Yu, Fan Wu, Jian Xu, and Kun Gai. 2021. Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 993--1001.

Digital Library

Cited By

Hu YHan CGuo TXiao HDastani MSichman JAlechina NDignum V(2024)Applying Opponent Modeling for Automatic Bidding in Online Repeated AuctionsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662938(843-851)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662938
Li XWang ZZhu BHe FWang YWang XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in FeedProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657774(1211-1220)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657774

Index Terms

Learning-Based Ad Auction Design with Externalities: The Framework and A Matching-Based Approach
1. Information systems
  1. Information systems applications
    1. Computational advertising
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 ACM.

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Publication History

Published: 04 August 2023

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National Natural Science Foundation of China

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KDD '23

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Hu YHan CGuo TXiao HDastani MSichman JAlechina NDignum V(2024)Applying Opponent Modeling for Automatic Bidding in Online Repeated AuctionsProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3662938(843-851)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3662938
Li XWang ZZhu BHe FWang YWang XHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Deep Automated Mechanism Design for Integrating Ad Auction and Allocation in FeedProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657774(1211-1220)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657774

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