research-article

Good abandonment in mobile and PC internet search

Authors:

Akihito TokudaAuthors Info & Claims

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

Pages 43 - 50

https://doi.org/10.1145/1571941.1571951

Published: 19 July 2009 Publication History

Abstract

Query abandonment by search engine users is generally considered to be a negative signal. In this paper, we explore the concept of good abandonment. We define a good abandonment as an abandoned query for which the user's information need was successfully addressed by the search results page, with no need to click on a result or refine the query. We present an analysis of abandoned internet search queries across two modalities (PC and mobile) in three locales. The goal is to approximate the prevalence of good abandonment, and to identify types of information needs that may lead to good abandonment, across different locales and modalities. Our study has three key findings: First, queries potentially indicating good abandonment make up a significant portion of all abandoned queries. Second, the good abandonment rate from mobile search is significantly higher than that from PC search, across all locales tested. Third, classified by type of information need, the major classes of good abandonment vary dramatically by both locale and modality. Our findings imply that it is a mistake to uniformly consider query abandonment as a negative signal. Further, there is a potential opportunity for search engines to drive additional good abandonment, especially for mobile search users, by improving search features and result snippets.

References

[1]

Google search features: http://www.google.com/intl/en/help/features.html.

[2]

Yahoo! shortcuts services: http://help.yahoo.com/l/us/yahoo/search/basics/basics--05.html.

[3]

E. Agichtein, E. Brill, and E. Dumais. Improving web search ranking by incorporating user behavior information. In Proceedings of the 29th annual international ACM SIGIR, pages 19 -- 26. ACM, 2006.

Digital Library

[4]

R. Baeza-Yates, G. Dupret, and J. Velasco. A study of mobile search queries in japan. In Proceedings of the International World Wide Web Conference, 2007.

[5]

B. Carterette and R. Jones. Evaluating web search engines using clickthrough data. Submitted to SIGIR, 2007. {6} K. Church, B. Smyth, P. Cotter, and K. Bradley. Mobile information access: A study of emerging search behavior on the mobile internet. ACM Transactions on the Web, 1(1), 2007.

Digital Library

[6]

E. Cutrell and Z. Guan. What are you looking for?: an eye-tracking study of information usage in web search. In proceedings of the SIGCHI conference on Human factors in computing systems, pages 407 -- 416, 2007.

Digital Library

[7]

T. Joachims. Evaluating retrieval performance using clickthrough data. In Proceedings of the SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval, 2002.

[8]

T. Joachims, L. A. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 154 -- 161. ACM, 2005.

Digital Library

[9]

T. Joachims and F. Radlinski. Search engines that learn from implicit feedback. Computer, 40: 34 -- 40, 2007.

Digital Library

[10]

M. Kamvar and S. Baluja. A large scale study of wireless search behavior: Google mobile search. In Proceedings of the SIGCHI conference on Human Factors in computing systems, pages 701 -- 709, 2006.

Digital Library

[11]

M. Kamvar, M. Kellar, P. R., and Y. Xu. Computers and iphones and mobile phones, oh my! a logs--based comparison of search users on different devices. WWW, 2008.

Digital Library

[12]

F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In Proceeding of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pages 239 -- 248. ACM, 2005.

Digital Library

[13]

F. Radlinski and T. Joachims. Active exploration for learning rankings from clickthrough data. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 570 -- 579. ACM, 2007.

Digital Library

[14]

A. Sarma, S. Gollapudi, and S. Ieong. Bypass rates: Reducing query abandonment using negative inferences. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 177 -- 185, 2008.

Digital Library

[15]

T. Sohn, K. Li, W. Griswold, and J. Hollan. A diary study of mobile information needs. In Proceeding of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pages 433 -- 443. ACM, 2008.

Digital Library

[16]

A. Turpin, Y. Tsegay, D. Hawking, and H. E. Williams. Fast generation of result snippets in web search. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pages 127 -- 134. ACM, 2007.

Digital Library

[17]

G. R. Xue, H. J. Zeng, Z. Chen, Y. Yu, W. Y. Ma, W. S. Xi, and W. G. Fan. Optimizing web search using web click--through data. In Proceedings of the thirteenth ACM international conference on Information and knowledge management, pages 118 -- 126. ACM, 2004.

Digital Library

[18]

X. Xue, Z. Zhou, and Z. Zhang. Improve web search using image snippets. In Proceedings of the National Conference on Artificial. ACM, 2006.

Digital Library

[19]

K. Yamauchi, C. W., and W. D. A study on japanese mobile phone market and its applications. In Computer and Information Technology, The Fourth International Conference, pages 875 -- 878, 2004.

Digital Library

Cited By

Pergantis MVarlamis IKanellopoulos NGiannakoulopoulos A(2023)Searching Online for Art and Culture: User Behavior AnalysisFuture Internet10.3390/fi1506021115:6(211)Online publication date: 11-Jun-2023
https://doi.org/10.3390/fi15060211
Pergantis MKouretsis AGiannakoulopoulos A(2023)Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning TechniquesAnalytics10.3390/analytics20200212:2(359-392)Online publication date: 26-Apr-2023
https://doi.org/10.3390/analytics2020021
Ye ZXie XAi QLiu YWang ZSu WZhang M(2023)Relevance Feedback with Brain SignalsACM Transactions on Information Systems10.1145/363787442:4(1-37)Online publication date: 18-Dec-2023
https://dl.acm.org/doi/10.1145/3637874
Show More Cited By

Index Terms

Good abandonment in mobile and PC internet search
1. Information systems
  1. Information retrieval

Recommendations

Learning to Account for Good Abandonment in Search Success Metrics
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Abandonment in web search has been widely used as a proxy to measure user satisfaction. Initially it was considered a signal of dissatisfaction, however with search engines moving towards providing answer-like results, a new category of abandonment was ...
Detecting Good Abandonment in Mobile Search
WWW '16: Proceedings of the 25th International Conference on World Wide Web

Web search queries for which there are no clicks are referred to as abandoned queries and are usually considered as leading to user dissatisfaction. However, there are many cases where a user may not click on any search result page (SERP) but still be ...
Context-aware web search abandonment prediction
SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

Web search queries without hyperlink clicks are often referred to as abandoned queries. Understanding the reasons for abandonment is crucial for search engines in evaluating their performance. Abandonment can be categorized as good or bad depending on ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '09: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval

July 2009

896 pages

ISBN:9781605584836

DOI:10.1145/1571941

General Chairs:
James Allan
University of Massachusetts Amherst, USA
,
Javed Aslam
Northeastern University, USA
,
Program Chairs:
Mark Sanderson
University of Sheffield, UK
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2009 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: 19 July 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGIR '09

Sponsor:

SIGIR '09: The 32nd International ACM SIGIR conference on research and development in Information Retrieval

July 19 - 23, 2009

MA, Boston, USA

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

104
Total Citations
View Citations
1,375
Total Downloads

Downloads (Last 12 months)24
Downloads (Last 6 weeks)1

Reflects downloads up to 27 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pergantis MVarlamis IKanellopoulos NGiannakoulopoulos A(2023)Searching Online for Art and Culture: User Behavior AnalysisFuture Internet10.3390/fi1506021115:6(211)Online publication date: 11-Jun-2023
https://doi.org/10.3390/fi15060211
Pergantis MKouretsis AGiannakoulopoulos A(2023)Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning TechniquesAnalytics10.3390/analytics20200212:2(359-392)Online publication date: 26-Apr-2023
https://doi.org/10.3390/analytics2020021
Ye ZXie XAi QLiu YWang ZSu WZhang M(2023)Relevance Feedback with Brain SignalsACM Transactions on Information Systems10.1145/363787442:4(1-37)Online publication date: 18-Dec-2023
https://dl.acm.org/doi/10.1145/3637874
Oliveira BTeixeira Lopes C(2023)The Evolution of Web Search User Interfaces - An Archaeological Analysis of Google Search Engine Result PagesProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578320(55-68)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578320
Zhou DLiu LAnubhai AShandilya MSigalas SWang WHuang Z(2023)Beyond Accurate Answers: Evaluating Open-Domain Question Answering in Enterprise SearchProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578314(308-312)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578314
Shih C(2023)Navigating the Web10.1017/9781009122924Online publication date: 3-Apr-2023
https://doi.org/10.1017/9781009122924
Kashyapi SDietz L(2022)Topic-Mono-BERT: A Joint Retrieval-Clustering System for Retrieving Overview PassagesProceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3574318.3574336(54-59)Online publication date: 9-Dec-2022
https://dl.acm.org/doi/10.1145/3574318.3574336
Abualsaud MSmucker MElsweiler DKruschwitz ULudwig B(2022)The Dark Side of Relevance: The Effect of Non-Relevant Results on Search BehaviorProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505770(1-11)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505770
Bink MZimmerman SElsweiler DElsweiler DKruschwitz ULudwig B(2022)Featured Snippets and their Influence on Users’ Credibility JudgementsProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505766(113-122)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505766
Ye ZXie XLiu YWang ZLi XLi JChen XZhang MMa SAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Why Don't You ClickProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532082(633-645)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3532082
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