More Web Proxy on the site http://driver.im/

research-article

Ad Auction Design with Coupon-Dependent Conversion Rate in the Auto-bidding World

Authors:

Pingzhong Tang,

Zhe MaAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 3417 - 3427

https://doi.org/10.1145/3543507.3583230

Published: 30 April 2023 Publication History

Abstract

Online advertising has become a dominant source of revenue of the Internet. In classic auction theory, only the auctioneer (i.e., the platform) and buyers (i.e., the advertisers) are involved, while the advertising audiences are ignored. For ecommerce advertising, however, the platform can provide coupons for the advertising audiences and nudge them into purchasing more products at lower prices (e.g., 2 dollars off the regular price). Such promotions can lead to an increase in amount and value of purchases. In this paper, we jointly design the coupon value computation, slot allocation, and payment of online advertising in an auto-bidding world. Firstly, we propose the auction mechanism, named CFA-auction (i.e., Coupon-For-the-Audiences-auction), which takes advertising audiences into account in the auction design. We prove the existence of pacing equilibrium, and show that CFA-auction satisfies the IC (incentive compatibility), IR (individual rationality) constraints. Then, we study the optimality of CFA-auction, and prove it can maintain an approximation of the optimal. Finally, experimental evaluation results on both offline dataset as well as online A/B test demonstrate the effectiveness of CFA-auction.

References

[1]

Gagan Aggarwal, Ashwinkumar Badanidiyuru, and Aranyak Mehta. 2019. Autobidding with constraints. In International Conference on Web and Internet Economics. Springer, 17–30.

[2]

Moshe Babaioff, Richard Cole, Jason Hartline, Nicole Immorlica, and Brendan Lucier. 2021. Non-Quasi-Linear Agents in Quasi-Linear Mechanisms. In 12th Innovations in Theoretical Computer Science Conference (ITCS 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.

[3]

Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, and Song Zuo. 2021. Robust Auction Design in the Auto-bidding World. Advances in Neural Information Processing Systems 34 (2021).

[4]

Santiago R Balseiro, Yuan Deng, Jieming Mao, Vahab S Mirrokni, and Song Zuo. 2021. The Landscape of Auto-bidding Auctions: Value versus Utility Maximization. In Proceedings of the 22nd ACM Conference on Economics and Computation. 132–133.

Digital Library

[5]

Santiago R Balseiro and Yonatan Gur. 2019. Learning in repeated auctions with budgets: Regret minimization and equilibrium. Management Science 65, 9 (2019), 3952–3968.

Digital Library

[6]

Dirk Bergemann, Paul Dütting, Renato Paes Leme, and Song Zuo. 2022. Calibrated Click-Through Auctions. In WWW ’22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, Frédérique Laforest, Raphaël Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Médini (Eds.). ACM, 47–57. https://doi.org/10.1145/3485447.3512050

Digital Library

[7]

David Berger, Konstantin Milbradt, Fabrice Tourre, and Joseph Vavra. 2021. Mortgage prepayment and path-dependent effects of monetary policy. American Economic Review 111, 9 (2021), 2829–78.

[8]

Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, and Yiwei Zhang. 2018. Reinforcement Mechanism Design for e-commerce. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, April 23-27, 2018, Pierre-Antoine Champin, Fabien Gandon, Mounia Lalmas, and Panagiotis G. Ipeirotis (Eds.). ACM, 1339–1348. https://doi.org/10.1145/3178876.3186039

Digital Library

[9]

Qingpeng Cai, Aris Filos-Ratsikas, Pingzhong Tang, and Yiwei Zhang. 2018. Reinforcement mechanism design for fraudulent behaviour in e-commerce. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.

[10]

Jin Chen, Ju Xu, Gangwei Jiang, Tiezheng Ge, Zhiqiang Zhang, Defu Lian, and Kai Zheng. 2021. Automated Creative Optimization for E-Commerce Advertising. In WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, Jure Leskovec, Marko Grobelnik, Marc Najork, Jie Tang, and Leila Zia (Eds.). ACM / IW3C2, 2304–2313. https://doi.org/10.1145/3442381.3449909

Digital Library

[11]

Xi Chen, Christian Kroer, and Rachitesh Kumar. 2021. The Complexity of Pacing for Second-Price Auctions. In Proceedings of the 22nd ACM Conference on Economics and Computation. 318–318.

Digital Library

[12]

Edward H Clarke. 1971. Multipart pricing of public goods. Public choice (1971), 17–33.

[13]

Vincent Conitzer, Christian Kroer, Debmalya Panigrahi, Okke Schrijvers, Eric Sodomka, Nicolás E Stier Moses, and Chris Wilkens. 2019. Pacing Equilibrium in First-Price Auction Markets. In EC.

[14]

Vincent Conitzer, Christian Kroer, Eric Sodomka, and N. Stier-Moses. 2018. Multiplicative Pacing Equilibria in Auction Markets. Oper. Res. 70 (2018), 963–989.

Digital Library

[15]

Yuan Deng, Jieming Mao, Vahab Mirrokni, and Song Zuo. 2021. Towards efficient auctions in an auto-bidding world. In Proceedings of the Web Conference 2021. 3965–3973.

Digital Library

[16]

Shahar Dobzinski and Renato Paes Leme. 2013. Efficiency Guarantees in Auctions with Budgets. CoRR abs/1304.7048 (2013). arXiv:1304.7048http://arxiv.org/abs/1304.7048

[17]

Paul Duetting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, and Sai Srivatsa Ravindranath. 2019. Optimal Auctions through Deep Learning. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA(Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 1706–1715. http://proceedings.mlr.press/v97/duetting19a.html

[18]

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 97, 1 (2007), 242–259.

[19]

Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep session interest network for click-through rate prediction. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2301–2307.

[20]

Zhe Feng, Harikrishna Narasimhan, and David C. Parkes. 2018. Deep Learning for Revenue-Optimal Auctions with Budgets. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018, Elisabeth André, Sven Koenig, Mehdi Dastani, and Gita Sukthankar (Eds.). International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM, 354–362. http://dl.acm.org/citation.cfm¿id=3237439

[21]

Avi Goldfarb and Catherine E. Tucker. 2015. Standardization and the Effectiveness of Online Advertising. Manag. Sci. 61, 11 (2015), 2707–2719. https://doi.org/10.1287/mnsc.2014.2016

Digital Library

[22]

Negin Golrezaei, Ilan Lobel, and Renato Paes Leme. 2021. Auction design for roi-constrained buyers. In Proceedings of the Web Conference 2021. 3941–3952.

Digital Library

[23]

Theodore Groves. 1973. Incentives in teams. Econometrica: Journal of the Econometric Society (1973), 617–631.

[24]

John R. Hauser, Guilherme Liberali, and Glen L. Urban. 2014. Website Morphing 2.0: Switching Costs, Partial Exposure, Random Exit, and When to Morph. Manag. Sci. 60, 6 (2014), 1594–1616. https://doi.org/10.1287/mnsc.2014.1961

Digital Library

[25]

Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, and Joaquin Quiñonero Candela. 2014. Practical Lessons from Predicting Clicks on Ads at Facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, ADKDD 2014, August 24, 2014, New York City, New York, USA, Esin Saka, Dou Shen, Kuang-chih Lee, and Ying Li (Eds.). ACM, 5:1–5:9. https://doi.org/10.1145/2648584.2648589

Digital Library

[26]

Kuang-chih Lee, Burkay Orten, Ali Dasdan, and Wentong Li. 2012. Estimating conversion rate in display advertising from past erformance data. In The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, Beijing, China, August 12-16, 2012, Qiang Yang, Deepak Agarwal, and Jian Pei (Eds.). ACM, 768–776. https://doi.org/10.1145/2339530.2339651

Digital Library

[27]

Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, Yiqing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen, and Xiaoqiang Zhu. 2021. Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising. In KDD ’21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021, Feida Zhu, Beng Chin Ooi, and Chunyan Miao (Eds.). ACM, 3354–3364. https://doi.org/10.1145/3447548.3467103

Digital Library

[28]

Aranyak Mehta. 2022. Auction design in an auto-bidding setting: Randomization improves efficiency beyond VCG. In Proceedings of the ACM Web Conference 2022. 173–181.

Digital Library

[29]

Yurii Nesterov. 2003. Introductory lectures on convex optimization: A basic course. Vol. 87. Springer Science & Business Media.

[30]

Weiran Shen, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, and Pingzhong Tang. 2020. Reinforcement mechanism design: With applications to dynamic pricing in sponsored search auctions. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 2236–2243.

[31]

Weiran Shen, Pingzhong Tang, and Song Zuo. 2019. Automated Mechanism Design via Neural Networks. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS ’19, Montreal, QC, Canada, May 13-17, 2019, Edith Elkind, Manuela Veloso, Noa Agmon, and Matthew E. Taylor (Eds.). International Foundation for Autonomous Agents and Multiagent Systems, 215–223. http://dl.acm.org/citation.cfm¿id=3331696

Digital Library

[32]

Pingzhong Tang. 2017. Reinforcement mechanism design. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19-25, 2017, Carles Sierra (Ed.). ijcai.org, 5146–5150. https://doi.org/10.24963/ijcai.2017/739

[33]

William Vickrey. 1961. Counterspeculation, auctions, and competitive sealed tenders. The Journal of finance 16, 1 (1961), 8–37.

[34]

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 WSDM ’21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021, Liane Lewin-Eytan, David Carmel, Elad Yom-Tov, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 993–1001. https://doi.org/10.1145/3437963.3441771

Digital Library

[35]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 1059–1068.

Digital Library

Cited By

Liaw CMehta AZhu WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Efficiency of Non-Truthful Auctions in Auto-bidding with Budget ConstraintsProceedings of the ACM Web Conference 202410.1145/3589334.3645636(223-234)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645636

Index Terms

Ad Auction Design with Coupon-Dependent Conversion Rate in the Auto-bidding World
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

Recommendations

Auctions without commitment in the auto-bidding world
WWW '23: Proceedings of the ACM Web Conference 2023

Advertisers in online ad auctions are increasingly using auto-bidding mechanisms to bid into auctions instead of directly bidding their value manually. One of the prominent auto-bidding formats is that of target cost-per-acquisition (tCPA) which ...
Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization
WWW '23: Proceedings of the ACM Web Conference 2023

Auto-bidding is now widely adopted as an interface between advertisers and internet advertising as it allows advertisers to specify high-level goals, such as maximizing value subject to a value-per-spend constraint. Prior research has mainly focused on ...
The Landscape of Auto-bidding Auctions: Value versus Utility Maximization
EC '21: Proceedings of the 22nd ACM Conference on Economics and Computation

Internet advertisers are increasingly adopting automated bidders to buy advertising opportunities. Automated bidders simplify the procurement process by allowing advertisers to specify their goals and then bidding on their behalf in the auctions that ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 April 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

National Key Research and Development Program of China
National Natural Science Foundation of China

Conference

WWW '23

Sponsor:

SIGWEB

WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

Acceptance Rates

Overall Acceptance Rate 1,068 of 6,946 submissions, 15%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
369
Total Downloads

Downloads (Last 12 months)181
Downloads (Last 6 weeks)25

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liaw CMehta AZhu WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Efficiency of Non-Truthful Auctions in Auto-bidding with Budget ConstraintsProceedings of the ACM Web Conference 202410.1145/3589334.3645636(223-234)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645636

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents