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.

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Digital Library

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Index Terms

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

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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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2007

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WWW'07

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ACM

WWW'07: 16th International World Wide Web Conference

May 8 - 12, 2007

Alberta, Banff, Canada

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

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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
Wu SDu LYang JWang YZhan DZhao SSun ZKiyavash NMooij J(2024)RE-SORTProceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence10.5555/3702676.3702854(3816-3828)Online publication date: 15-Jul-2024
https://dl.acm.org/doi/10.5555/3702676.3702854
Zhang XQin HChou MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Online resource allocation with non-stationary customersProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694537(59700-59730)Online publication date: 21-Jul-2024
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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
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