research-article

Personalized click prediction in sponsored search

Authors:

Erick Cantú-PazAuthors Info & Claims

WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

Pages 351 - 360

https://doi.org/10.1145/1718487.1718531

Published: 04 February 2010 Publication History

Abstract

Sponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to sponsored search because the prediction is used to influence ranking, filtering, placement, and pricing of ads. Ad ranking, filtering and placement have a direct impact on the user experience, as users expect the most useful ads to rank high and be placed in a prominent position on the page. Pricing impacts the advertisers' return on their investment and revenue for the search engine. The objective of this paper is to present a framework for the personalization of click models in sponsored search. We develop user-specific and demographic-based features that reflect the click behavior of individuals and groups. The features are based on observations of search and click behaviors of a large number of users of a commercial search engine. We add these features to a baseline non-personalized click model and perform experiments on offline test sets derived from user logs as well as on live traffic. Our results demonstrate that the personalized models significantly improve the accuracy of click prediction.

References

[1]

V. Abhishek and K. Hosanagar. Keyword generation for search engine advertising using semantic similarity between terms. In Proceedings of the ninth international conference on Electronic commerce, pages 89--94, New York, NY, USA, 2007. ACM.

Digital Library

[2]

E. Agichtein, E. Brill, and S. Dumais. Improving web search ranking by incorporating user behavior information. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pages 19--26, New York, NY, USA, 2006. ACM.

Digital Library

[3]

J. Attenberg, S. Pandey, and T. Suel. Modeling and predicting user behavior in sponsored search. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1067--1076, New York, NY, USA, 2009. ACM.

Digital Library

[4]

A.L. Berger and V.J.D. Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 22:39--71, 1996.

Digital Library

[5]

D. Chakrabarti, D. Agarwal, and V. Josifovski. Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pages 281--297, Berkeley, CA, 1967. University of California Press.

[6]

D. Chakrabarti, D. Agarwal, and V. Josifovski. Contextual advertising by combining relevance with click feedback. In Proceeding of the 17th international conference on World Wide Web, pages 417--426, New York, NY, USA, 2008. ACM.

Digital Library

[7]

S.F. Chen and R. Rosenfeld. A gaussian prior for smoothing maximum entropy models. Technical report, Carnegie Mellon University, 1999.

[8]

M. Ciaramita, V. Murdock, and V. Plachouras. Online learning from click data for sponsored search. In Proceeding of the 17th international conference on World Wide Web, pages 227--236, New York, NY, USA, 2008. ACM.

Digital Library

[9]

N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey. An experimental comparison of click position-bias models. In Proceedings of the international conference on Web search and web data mining, pages 87--94, New York, NY, USA, 2008. ACM.

Digital Library

[10]

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

[11]

D.C. Fain and J.O. Pedersen. Sponsored search: a brief history. In Proceedings of 2nd Workshop on Sponsored Search Auctions, 2006.

[12]

Google. How are ads ranked? http://www.google.com/support/grants/bin/answer.py?hl=en&answer=98917.

[13]

B.J. Jansen and T. Mullen. Sponsored search: an overview of the concept, history, and technology. International Journal of Electronic Business, 6:114--131, 2008.

[14]

C. Liu, H. Wang, S. Mcclean, J. Liu, and S. Wu. Syntactic information retrieval. In Proceedings of the 2007 IEEE International Conference on Granular Computing, page 703, Washington, DC, USA, 2007. IEEE Computer Society.

Digital Library

[15]

T.P. Minka. A comparison of numerical optimizers for logistic regression. Technical report, Microsoft, 2003.

[16]

A. Mordecai. Nonlinear Programming: Analysis and Methods. Dover Publishing, Mineola, NY, 2003

Cited By

Selwon KSzymański J(2024)Enhancing Personalized Travel Recommendations: Integrating User Behavior and Content AnalysisProceedings of the 32nd International Conference on Information Systems Development10.62036/ISD.2024.49Online publication date: 2024
https://doi.org/10.62036/ISD.2024.49
Zhang WLi DLiang CZhou FZhang ZWang XLi RZhou YHuang YLiang DWang KWang ZChen ZWu FChen MLi HWu YShu ZYuan MReddy SChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in MetaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648301(47-55)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648301
Duffett RCharles J(2024)Assessing antecedents of Google shopping ads intention to purchase: a multigroup analysis of generation Y and ZYoung Consumers10.1108/YC-12-2023-1923Online publication date: 30-Apr-2024
https://doi.org/10.1108/YC-12-2023-1923
Show More Cited By

Index Terms

Personalized click prediction in sponsored search
1. Information systems
  1. World Wide Web
    1. Online advertising
    2. Web applications
      1. Electronic commerce

Recommendations

New features for query dependent sponsored search click prediction
WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Click prediction for sponsored search is an important problem for commercial search engines. Good click prediction algorithm greatly affects on the revenue of the search engine, user experience and brings more clicks to landing pages of advertisers. ...
Data-driven text features for sponsored search click prediction
ADKDD '09: Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising

Much search engine revenue comes from sponsored search ads displayed with algorithmic search results. To maximize revenue, it is essential to choose a good slate of ads for each query, requiring accurate prediction of whether or not users will click on ...
Online learning from click data for sponsored search
WWW '08: Proceedings of the 17th international conference on World Wide Web

Sponsored search is one of the enabling technologies for today's Web search engines. It corresponds to matching and showing ads related to the user query on the search engine results page. Users are likely to click on topically related ads and the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '10: Proceedings of the third ACM international conference on Web search and data mining

February 2010

468 pages

ISBN:9781605588896

DOI:10.1145/1718487

General Chairs:
Brian D. Davison
Lehigh University, USA
,
Torsten Suel
Polytechnic Institute of NYU, USA
,
Program Chairs:
Nick Craswell
Microsoft, USA
,
Bing Liu
University of Illinois, Chicago, USA

Copyright © 2010 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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 February 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WSDM'10

Sponsor:

WSDM'10: Third ACM International Conference on Web Search and Data Mining

February 4 - 6, 2010

New York, New York, USA

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

94
Total Citations
View Citations
1,043
Total Downloads

Downloads (Last 12 months)28
Downloads (Last 6 weeks)9

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Selwon KSzymański J(2024)Enhancing Personalized Travel Recommendations: Integrating User Behavior and Content AnalysisProceedings of the 32nd International Conference on Information Systems Development10.62036/ISD.2024.49Online publication date: 2024
https://doi.org/10.62036/ISD.2024.49
Zhang WLi DLiang CZhou FZhang ZWang XLi RZhou YHuang YLiang DWang KWang ZChen ZWu FChen MLi HWu YShu ZYuan MReddy SChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in MetaCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648301(47-55)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648301
Duffett RCharles J(2024)Assessing antecedents of Google shopping ads intention to purchase: a multigroup analysis of generation Y and ZYoung Consumers10.1108/YC-12-2023-1923Online publication date: 30-Apr-2024
https://doi.org/10.1108/YC-12-2023-1923
Liu JLiu J(2023)Back to the Fundamentals: Extend the Rational AssumptionsA Behavioral Economics Approach to Interactive Information Retrieval10.1007/978-3-031-23229-9_5(131-152)Online publication date: 18-Feb-2023
https://doi.org/10.1007/978-3-031-23229-9_5
Nabi RPfeiffer JCharles DKıcıman E(2022)Causal Inference in the Presence of Interference in Sponsored Search AdvertisingFrontiers in Big Data10.3389/fdata.2022.8885925Online publication date: 21-Jun-2022
https://doi.org/10.3389/fdata.2022.888592
Shi YShi Y(2022)Link AnalysisAdvances in Big Data Analytics10.1007/978-981-16-3607-3_8(433-475)Online publication date: 13-Jan-2022
https://doi.org/10.1007/978-981-16-3607-3_8
Zhao JHuang JDeng HChang YXia L(2021)Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search PersonalizationACM Transactions on Information Systems10.1145/347610640:3(1-24)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1145/3476106
Bai XAbasi REdizel BMantrach A(2021)Position-Aware Deep Character-Level CTR Prediction for Sponsored SearchIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.294188133:4(1722-1736)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TKDE.2019.2941881
Moriwaki DHayakawa YMatsui ASaito YMunemasa IShibata M(2021)A Real-World Implementation of Unbiased Lift-based Bidding System2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671800(1877-1888)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671800
Chatterjee SMishra RRaichandani SJoshi P(2021)Response Prediction and Ranking Models for Large-Scale Ecommerce SearchApplied Advanced Analytics10.1007/978-981-33-6656-5_17(199-218)Online publication date: 9-Jun-2021
https://doi.org/10.1007/978-981-33-6656-5_17
Show More Cited By

View Options

Get Access

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