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Potential good abandonment prediction

Authors:

Aleksandr Chuklin,

Pavel SerdyukovAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 485 - 486

https://doi.org/10.1145/2187980.2188089

Published: 16 April 2012 Publication History

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Abstract

Abandonment rate is one of the most broadly used online user satisfaction metrics. In this paper we discuss the notion of potential good abandonment, i.e. queries that may potentially result in user satisfaction without the need to click on search results (if search engine result page contains enough details to satisfy the user information need). We show, that we can train a classifier which is able to distinguish between potential good and bad abandonments with rather good results compared to our baseline. As a case study we show how to apply these ideas to IR evaluation and introduce a new metric for A/B-testing -- Bad Abandonment Rate.

References

[1]

L. Chilton and J. Teevan. Addressing people's information needs directly in a web search result page. In WWW'11.

Digital Library

Google Scholar

[2]

C. Hauff, F. D. Jong, and D. Hiemstra. A Survey of Pre-Retrieval Query Performance Predictors. CIKM'08.

Digital Library

Google Scholar

[3]

C. Hsu, C. Chang, and C. Lin. A practical guide to support vector classification, 2003.

Google Scholar

[4]

R. Kohavi, R. Henne, and D. Sommerfield. Practical guide to controlled experiments on the web: listen to your customers not to the hippo. In KDD'07.

Digital Library

Google Scholar

[5]

J. Li, S. Huffman, and A. Tokuda. Good abandonment in mobile and PC internet search. SIGIR'09.

Digital Library

Google Scholar

[6]

M. Pasca. Weakly-supervised discovery of named entities using web search queries. CIKM'07.

Digital Library

Google Scholar

Cited By

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Wu DZhang SO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)Prediction of Good Abandonment Behavior in Mobile SearchProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378007(407-411)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3378007
Kompan MKassak OBielikova M(2018)The Short-term User Modeling for Predictive ApplicationsJournal on Data Semantics10.1007/s13740-018-0095-1Online publication date: 6-Oct-2018
https://doi.org/10.1007/s13740-018-0095-1
Kawasaki MKang ISakai TLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Ranking Rich Mobile Verticals based on Clicks and AbandonmentProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133059(2127-2130)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133059
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Index Terms

Potential good abandonment prediction
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

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: 16 April 2012

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WWW 2012

Sponsor:

Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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

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Wu DZhang SO'Brien HFreund LArapakis IHoeber OLopatovska I(2020)Prediction of Good Abandonment Behavior in Mobile SearchProceedings of the 2020 Conference on Human Information Interaction and Retrieval10.1145/3343413.3378007(407-411)Online publication date: 14-Mar-2020
https://dl.acm.org/doi/10.1145/3343413.3378007
Kompan MKassak OBielikova M(2018)The Short-term User Modeling for Predictive ApplicationsJournal on Data Semantics10.1007/s13740-018-0095-1Online publication date: 6-Oct-2018
https://doi.org/10.1007/s13740-018-0095-1
Kawasaki MKang ISakai TLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Ranking Rich Mobile Verticals based on Clicks and AbandonmentProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133059(2127-2130)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133059
Williams KZitouni ILim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Does That Mean You're Happy?Proceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133035(727-736)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3133035
Khabsa MCrook AAwadallah AZitouni IAnastasakos TWilliams KMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Learning to Account for Good Abandonment in Search Success MetricsProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983867(1893-1896)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983867
Williams KKiseleva JCrook AZitouni IAwadallah AKhabsa MPerego RSebastiani FAslam JRuthven IZobel J(2016)Is This Your Final Answer?Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2914736(889-892)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2914736
Dan ODavison B(2016)Measuring and Predicting Search Engine Users’ SatisfactionACM Computing Surveys10.1145/289348649:1(1-35)Online publication date: 28-Jul-2016
https://dl.acm.org/doi/10.1145/2893486
Williams KKiseleva JCrook AZitouni IAwadallah AKhabsa MBourdeau JHendler JNkambou RHorrocks IZhao B(2016)Detecting Good Abandonment in Mobile SearchProceedings of the 25th International Conference on World Wide Web10.1145/2872427.2883074(495-505)Online publication date: 11-Apr-2016
https://dl.acm.org/doi/10.1145/2872427.2883074
Zengin MCarterette B(2016)Reformulate or Quit: Predicting User Abandonment in Ideal SessionsInformation Retrieval Technology10.1007/978-3-319-48051-0_26(322-328)Online publication date: 15-Oct-2016
https://doi.org/10.1007/978-3-319-48051-0_26
Lu WJia Y(2015)Inferring User Preference in Good Abandonment from Eye MovementsWeb-Age Information Management10.1007/978-3-319-21042-1_40(457-460)Online publication date: 6-Jun-2015
https://doi.org/10.1007/978-3-319-21042-1_40
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Learning to Account for Good Abandonment in Search Success Metrics

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