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

Article

Predicting clicks: estimating the click-through rate for new ads

Authors:

Matthew Richardson,

Ewa Dominowska,

Robert RagnoAuthors Info & Claims

WWW '07: Proceedings of the 16th international conference on World Wide Web

Pages 521 - 530

https://doi.org/10.1145/1242572.1242643

Published: 08 May 2007 Publication History

Abstract

Search engine advertising has become a significant element of the Web browsing experience. Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad. This ranking has a strong impact on the revenue the search engine receives from the ads. Further, showing the user an ad that they prefer to click on improves user satisfaction. For these reasons, it is important to be able to accurately estimate the click-through rate of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. We show that we can use features of ads, terms, and advertisers to learn a model that accurately predicts the click-though rate for new ads. We also show that using our model improves the convergence and performance of an advertising system. As a result, our model increases both revenue and user satisfaction.

References

[1]

E. Agichtein, E. Brill, S. Dumais, "Improving Web Search Ranking by Incorporating User Behavior Information", In World Wide Web, 2006.

[2]

L. Baker, "Google vs. Yahoo: Earnings Reports Comparison," In Search Engine Journal http://www.searchengine-journal.com/?p=3923.

[3]

K. Bartz, V. Murthi, S. Sebastian, "Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation", In Proceedings of the Second Workshop on Sponsored Search Auctions, 2006.

[4]

E. Burns, "SEMs Sees Optimization PPC", In ClickZ, http://www.clickz.com/showPage.html?page=3550881

[5]

Did-it, Enquiro, and Eyetools, "Eye Tracking Study", http://www.enquiro.com/eye-tracking-pr.asp

[6]

B. Edelman, M. Ostrovsky. "Strategic bidder behavior in sponsored search auctions." In Workshop on Sponsored Search Auctions, ACM Electronic Commerce, 2005.

[7]

D. Fain and J. Pedersen. "Sponsored Search: a Brief History", In Proceedings of the Second Workshop on Sponsored Search Auctions, 2006.

[8]

J. Feng, H. Bhargava, D. Pennock, "Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms" In Informs Journal on Computing, 2006.

Digital Library

[9]

J. Friedman. "Greedy Function Approximation: A Gradient Boosting Machine," Technical Report, Dept. of Statistics, Stanford University, 1999.

[10]

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Springer-Verlag, New York, 2001.

[11]

B. Jansen and M. Resnick, "Examining Searcher Perceptions of and Interactions with Sponsored Results," In Proceedings of the Workshop on Sponsored Search Auctions, 2005.

[12]

B. Kitts, P. Laxminarayan, B. LeBlanc, R. Meech, "A Formal Analysis of Search Auctions Including Predictions on Click Fraud and Bidding Tactics", In Workshop on Sponsored Search Auctions, ACM Electronic Commerce, 2005.

[13]

S. Kullback, R. A. Leibler, "On Information and Sufficiency", Annals of Mathematical Statistics, Vol. 22, No.1, pp. 79--86, 1951.

[14]

S. Lawrence, C. L. Giles, Searching the World Wide Web, Science 280, pp. 98--100, 1998.

[15]

S. Lawrence, C. L. Giles, Accessibility of information of the Web, Nature 400, pp. 107--109, 1999.

[16]

D. C. Liu and J. Nocedal, "On the limited memory BFGS method for large scale optimization," Mathematical Programming, vol. 45, no. 3, pp. 503--528, 1989.

Digital Library

[17]

D. Murrow, "Paid Search Ad Spend Will Hit $10 Billion By 2009" In eMarketer, http://www.emarketer.com/Article-.aspx?1003861.

[18]

J. Nocedal and S. J. Wright, Numerical Optimization. Springer-Verlag, 1999.

[19]

M. Regelson and D. Fain, "Predicting click-through rate using keyword clusters," In Proceedings of the Second Workshop on Sponsored Search Auctions, 2006.

[20]

M. Richardson, A. Prakash, E. Brill, "Beyond Page Rank: Machine Learning for Static Ranking, In World Wide Web, 2006.

Digital Library

Cited By

Xu EZhao KYu ZWang HRen SCui HLiang YGuo B(2025)Upper bound on the predictability of rating prediction in recommender systemsInformation Processing & Management10.1016/j.ipm.2024.10395062:1(103950)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103950
Zeng LAsif M(2024)Advertisement design in dynamic interactive scenarios using DeepFM and long short-term memory (LSTM)PeerJ Computer Science10.7717/peerj-cs.193710(e1937)Online publication date: 27-Mar-2024
https://doi.org/10.7717/peerj-cs.1937
Chao YZhu HZhou Y(2024)Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital MarketingJournal of Organizational and End User Computing10.4018/JOEUC.34209236:1(1-28)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.4018/JOEUC.342092
Show More Cited By

Index Terms

Predicting clicks: estimating the click-through rate for new ads
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

Recommendations

Predicting CTR of new ads via click prediction
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Predicting CTR of ads on the search result page is an urgent topic. The reason for this is that choosing the right advertisement greatly affects revenue of the search engine and advertisers and user's satisfaction. For ads with the large click history ...
Using boosted trees for click-through rate prediction for sponsored search
ADKDD '12: Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy

We describe a new approach to solving the click-through rate (CTR) prediction problem in sponsored search by means of MatrixNet, the proprietary implementation of boosted trees. This problem is of special importance for the search engine, because ...
Smart marketing or bait & switch: competitors' brands as keywords in online advertising
WICOW '10: Proceedings of the 4th workshop on Information credibility

The business models of major Internet search engines depend on online advertising, primarily in the form of search engine keyword advertising. In recent years, a controversy surrounding keyword advertising has gained notoriety worldwide, in both the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '07: Proceedings of the 16th international conference on World Wide Web

May 2007

1382 pages

ISBN:9781595936547

DOI:10.1145/1242572

General Chairs:
Carey Williamson
University of Calgary, Canada
,
Mary Ellen Zurko
IBM, USA
,
Program Chairs:
Peter Patel-Schneider
Bell Labs Research, USA
,
Prashant Shenoy
University of Massachusetts at Amherst, USA

Copyright © 2007 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 ACM 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

ACM: Association for Computing Machinery

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

WWW'07

Sponsor:

ACM

WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

748
Total Citations
View Citations
6,375
Total Downloads

Downloads (Last 12 months)472
Downloads (Last 6 weeks)57

Reflects downloads up to 11 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Xu EZhao KYu ZWang HRen SCui HLiang YGuo B(2025)Upper bound on the predictability of rating prediction in recommender systemsInformation Processing & Management10.1016/j.ipm.2024.10395062:1(103950)Online publication date: Jan-2025
https://doi.org/10.1016/j.ipm.2024.103950
Zeng LAsif M(2024)Advertisement design in dynamic interactive scenarios using DeepFM and long short-term memory (LSTM)PeerJ Computer Science10.7717/peerj-cs.193710(e1937)Online publication date: 27-Mar-2024
https://doi.org/10.7717/peerj-cs.1937
Chao YZhu HZhou Y(2024)Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital MarketingJournal of Organizational and End User Computing10.4018/JOEUC.34209236:1(1-28)Online publication date: 16-Apr-2024
https://dl.acm.org/doi/10.4018/JOEUC.342092
He LZhao JGu YElbaz MDing Z(2024)A bias study and an unbiased deep neural network for recommender systemsWeb Intelligence10.3233/WEB-23003622:1(15-29)Online publication date: 26-Mar-2024
https://doi.org/10.3233/WEB-230036
Yan CLi XTao RZhang ZWan Y(2024)MeFiNet: Modeling multi-semantic convolution-based feature interactions for CTR predictionIntelligent Data Analysis10.3233/IDA-22711328:1(261-278)Online publication date: 3-Feb-2024
https://doi.org/10.3233/IDA-227113
Guo SLiao XMeng FZhao QTang YLi HZong Q(2024)FSASA: Sequential recommendation based on fusing session-aware models and self-attention networksComputer Science and Information Systems10.2298/CSIS230522067G21:1(1-20)Online publication date: 2024
https://doi.org/10.2298/CSIS230522067G
Inoue DMatsumoto S(2024)Proposal of a CNN-based Method for Predicting the Number of Clicks on Micro-event Flyer ImagesCNNを用いたマイクロイベント告知画像のクリック数予測手法の提案IEEJ Transactions on Electronics, Information and Systems10.1541/ieejeiss.144.942144:9(942-954)Online publication date: 1-Sep-2024
https://doi.org/10.1541/ieejeiss.144.942
Li HSang LZhang YZhang XZhang Y(2024)CETN: Contrast-enhanced Through Network for Click-Through Rate PredictionACM Transactions on Information Systems10.1145/368857143:1(1-34)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688571
Arabzadeh NDiaz FHe JSakai TIshita EOhshima HRadboud University, Netherlands FMao JJose J(2024)Offline Evaluation of Set-Based Text-to-Image GenerationProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698424(42-53)Online publication date: 8-Dec-2024
https://dl.acm.org/doi/10.1145/3673791.3698424
Xi YLiu WDai XTang RLiu QZhang WYu Y(2024)Utility-Oriented Reranking with Counterfactual ContextACM Transactions on Knowledge Discovery from Data10.1145/367100418:8(1-22)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3671004
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.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents