Article

Estimating Ad Clickthrough Rate through Query Intent Analysis

Authors:

Charles L. A. Clarke,

Eugene Agichtein,

Qi GuoAuthors Info & Claims

WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01

Pages 222 - 229

https://doi.org/10.1109/WI-IAT.2009.39

Published: 15 September 2009 Publication History

Abstract

Clickthrough rate, bid, and cost-per-click are known to be among the factors that impact the rank of an ad shown on a search result page. Search engines can benefit from estimating ad clickthrough in order to determine the quality of ads and maximize their revenue. In this paper, a methodology is developed to estimate ad clickthrough rate by exploring user queries and clickthrough logs. As we demonstrate, the average ad clickthrough rate depends to a substantial extent on the rank position of ads and on the total number of ads displayed on the page. This observation is utilized by a baseline model to calculate the expected clickthrough rate for various ads. We further study the impact of query intent on the clickthrough rate, where query intent is predicted using a combination of query features and the content of search engine result pages. The baseline model and the query intent model are compared for the purpose of calculating the expected ad clickthrough rate. Our findings suggest that such factors as the rank of an ad, the number of ads displayed on the result page, and query intent are effective in estimating ad clickthrough rate.

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Cited By

Dave KVarma V(2014)Computational AdvertisingFoundations and Trends in Information Retrieval10.1561/15000000458:4–5(263-418)Online publication date: 29-Oct-2014
https://dl.acm.org/doi/10.1561/1500000045
Chapelle OManavoglu ERosales R(2014)Simple and Scalable Response Prediction for Display AdvertisingACM Transactions on Intelligent Systems and Technology10.1145/25321285:4(1-34)Online publication date: 29-Dec-2014
https://dl.acm.org/doi/10.1145/2532128
Ashkan AClarke C(2013)Impact of query intent and search context on clickthrough behavior in sponsored searchKnowledge and Information Systems10.1007/s10115-012-0485-x34:2(425-452)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10115-012-0485-x
Show More Cited By

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Estimating Ad Clickthrough Rate through Query Intent Analysis

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Published In

cover image ACM Conferences

WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01

September 2009

726 pages

ISBN:9780769538013

Sponsors

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

IEEE Computer Society

United States

Publication History

Published: 15 September 2009

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Cited By

Dave KVarma V(2014)Computational AdvertisingFoundations and Trends in Information Retrieval10.1561/15000000458:4–5(263-418)Online publication date: 29-Oct-2014
https://dl.acm.org/doi/10.1561/1500000045
Chapelle OManavoglu ERosales R(2014)Simple and Scalable Response Prediction for Display AdvertisingACM Transactions on Intelligent Systems and Technology10.1145/25321285:4(1-34)Online publication date: 29-Dec-2014
https://dl.acm.org/doi/10.1145/2532128
Ashkan AClarke C(2013)Impact of query intent and search context on clickthrough behavior in sponsored searchKnowledge and Information Systems10.1007/s10115-012-0485-x34:2(425-452)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10115-012-0485-x
Wang CChen HChen XLebanon GWang HZaki M(2012)Learning to predict the cost-per-click for your ad wordsProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398623(2291-2294)Online publication date: 29-Oct-2012
https://dl.acm.org/doi/10.1145/2396761.2398623
Rosales RCheng HManavoglu EAdar ETeevan JAgichtein EMaarek Y(2012)Post-click conversion modeling and analysis for non-guaranteed delivery display advertisingProceedings of the fifth ACM international conference on Web search and data mining10.1145/2124295.2124333(293-302)Online publication date: 8-Feb-2012
https://dl.acm.org/doi/10.1145/2124295.2124333
Teevan JLiebling DRavichandran Geetha GKing INejdl WLi H(2011)Understanding and predicting personal navigationProceedings of the fourth ACM international conference on Web search and data mining10.1145/1935826.1935848(85-94)Online publication date: 9-Feb-2011
https://dl.acm.org/doi/10.1145/1935826.1935848
Pandey SPunera KFontoura MJosifovski VCrestani FMarchand-Maillet SChen HEfthimiadis ESavoy J(2010)Estimating advertisability of tail queries for sponsored searchProceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval10.1145/1835449.1835544(563-570)Online publication date: 19-Jul-2010
https://dl.acm.org/doi/10.1145/1835449.1835544
Ashkan AClarke CCheung DSong IChu WHu XLin J(2009)Characterizing commercial intentProceedings of the 18th ACM conference on Information and knowledge management10.1145/1645953.1645965(67-76)Online publication date: 2-Nov-2009
https://dl.acm.org/doi/10.1145/1645953.1645965

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