research-article

Offline Evaluation of Response Prediction in Online Advertising Auctions

Author:

Olivier ChapelleAuthors Info & Claims

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

Pages 919 - 922

https://doi.org/10.1145/2740908.2742566

Published: 18 May 2015 Publication History

Get Access

Abstract

Click-through rates and conversion rates are two core machine learning problems in online advertising. The evaluation of such systems is often based on traditional supervised learning metrics that ignore how the predictions are used. These predictions are in fact part of bidding systems in online advertising auctions. We present here an empirical evaluation of a metric that is specifically tailored for auctions in online advertising and show that it correlates better than standard metrics with A/B test results.

References

[1]

O. Chapelle, E. Manavoglu, and R. Rosales. Simple and scalable response prediction for display advertising. ACM Transactions on Intelligent Systems and Technology, 5(4), 2014.

Digital Library

Google Scholar

[2]

O. Chapelle, J. Weston, L. Bottou, and V. Vapnik. Vicinal risk minimization. Advances in neural information processing systems, pages 416--422, 2001.

Google Scholar

[3]

B. Edelman, M. Ostrovsky, and M. Schwarz. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. American Economic Review, 97(1):242--259, 2007.

Crossref

Google Scholar

[4]

A. Ghosh, P. McAfee, K. Papineni, and S. Vassilvitskii. Bidding for representative allocations for display advertising. CoRR, abs/0910.0880, 2009.

Google Scholar

[5]

P. Hummel and P. McAfee. Loss functions for predicted click through rates in auctions for online advertising. Unpublished, 2013.

Google Scholar

[6]

L. Maaten, M. Chen, S. Tyree, and K. Q. Weinberger. Learning with marginalized corrupted features. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pages 410--418, 2013.

Google Scholar

[7]

H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, S. Chikkerur, D. Liu, M. Wattenberg, A. M. Hrafnkelsson, T. Boulos, and J. Kubica. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2013.

Digital Library

Google Scholar

[8]

J. Yi, Y. Chen, J. Li, S. Sett, and T. W. Yan. Predictive model performance: Offline and online evaluations. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1294--1302. ACM, 2013.

Digital Library

Google Scholar

[9]

S. Yuan, J. Wang, and X. Zhao. Real-time bidding for online advertising: Measurement and analysis. In Proceedings of the Seventh International Workshop on Data Mining for Online Advertising, ADKDD '13, 2013.

Digital Library

Google Scholar

Cited By

View all

Gigli MStella F(2024)Multi-armed bandits for performance marketingInternational Journal of Data Science and Analytics10.1007/s41060-023-00493-7Online publication date: 19-Jan-2024
https://doi.org/10.1007/s41060-023-00493-7
Lyu BFeng ZRobertson ZKoyejo SKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Pairwise ranking losses of click-through rates prediction for welfare maximization in ad auctionsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619375(23239-23263)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619375
Kvуetnyy RBunyak YSofina OKaduk OMamyrbayev OBaklaiev VYeraliyeva B(2023)ADVERTISING BIDDING OPTIMIZATION BY TARGETING BASED ON SELF-LEARNING DATABASEInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska10.35784/iapgos.537613:4(66-72)Online publication date: 20-Dec-2023
https://doi.org/10.35784/iapgos.5376
Show More Cited By

Index Terms

Offline Evaluation of Response Prediction in Online Advertising Auctions
1. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results

Recommendations

Display Advertising Auctions with Arbitrage

Online display advertising exchanges connect Web publishers with advertisers seeking to place ads. In many cases, the advertiser obtains value from an ad impression (a viewing by a user) only if it is clicked, and frequently advertisers prefer to pay ...
Online Advertising: Experimental Facts on Ethics, Involvement, and Product Type

The purpose of this chapter is to provide some insights into advertisements on the Iranian websites. Firstly, in publisher side, is the ethic a matter of fact in accepting Internet advertisements to publish? Second, to provide a preliminary insight into ...
An economic analysis of online advertising using behavioral targeting

Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and ...

Comments

Information & Contributors

Information

Published In

WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web

May 2015

1602 pages

ISBN:9781450334730

DOI:10.1145/2740908

General Chairs:
Aldo Gangemi
National Research Council, Italy & Paris 13 University-CNRS, France
,
Stefano Leonardi
Sapienza University of Rome, Italy
,
Alessandro Panconesi
Sapienza University of Rome, Italy

In-Cooperation

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '15

Sponsor:

IW3C2

WWW '15: 24th International World Wide Web Conference

May 18 - 22, 2015

Florence, Italy

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

23
Total Citations
View Citations
325
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Gigli MStella F(2024)Multi-armed bandits for performance marketingInternational Journal of Data Science and Analytics10.1007/s41060-023-00493-7Online publication date: 19-Jan-2024
https://doi.org/10.1007/s41060-023-00493-7
Lyu BFeng ZRobertson ZKoyejo SKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Pairwise ranking losses of click-through rates prediction for welfare maximization in ad auctionsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619375(23239-23263)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619375
Kvуetnyy RBunyak YSofina OKaduk OMamyrbayev OBaklaiev VYeraliyeva B(2023)ADVERTISING BIDDING OPTIMIZATION BY TARGETING BASED ON SELF-LEARNING DATABASEInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska10.35784/iapgos.537613:4(66-72)Online publication date: 20-Dec-2023
https://doi.org/10.35784/iapgos.5376
Wang PJiang LYang J(2023)The Early Impact of GDPR Compliance on Display Advertising: The Case of an Ad PublisherJournal of Marketing Research10.1177/0022243723117184861:1(70-91)Online publication date: 23-Jun-2023
https://doi.org/10.1177/00222437231171848
Sahllal NSouidi E(2023)A Comparative Analysis of Sampling Techniques for Click-Through Rate Prediction in Native AdvertisingIEEE Access10.1109/ACCESS.2023.325598311(24511-24526)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3255983
Cheng’ai SCaixia JLiqing QQingyu YChunxue Z(2023)PC-IEN: a click-through rate prediction method based on dynamic collaborative personalized interest extractionArtificial Intelligence Review10.1007/s10462-023-10447-x56:10(11123-11147)Online publication date: 11-Mar-2023
https://dl.acm.org/doi/10.1007/s10462-023-10447-x
Li XZhang MWang ZTong YZhang ARangwala H(2022)Arbitrary Distribution Modeling with Censorship in Real-Time Bidding AdvertisingProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539048(3250-3258)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539048
Fan RDelage EAl Hasan MXiong L(2022)Risk-Aware Bid Optimization for Online Display AdvertisementProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557436(457-467)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557436
Kim KKwon EPark J(2021)Deep User Segment Interest Network Modeling for Click-Through Rate Prediction of Online AdvertisingIEEE Access10.1109/ACCESS.2021.30498279(9812-9821)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3049827
Choi HMela CBalseiro SLeary A(2020)Online Display Advertising MarketsInformation Systems Research10.1287/isre.2019.090231:2(556-575)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1287/isre.2019.0902
Show More Cited By

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Display Advertising Auctions with Arbitrage

Online Advertising: Experimental Facts on Ethics, Involvement, and Product Type

An economic analysis of online advertising using behavioral targeting

Comments

Information

Published In

Sponsors

In-Cooperation

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations