research-article

A comparison of query and term suggestion features for interactive searching

Authors:

Karl Gyllstrom,

Earl W. BaileyAuthors Info & Claims

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

Pages 371 - 378

https://doi.org/10.1145/1571941.1572006

Published: 19 July 2009 Publication History

Abstract

Query formulation is one of the most difficult and important aspects of information seeking and retrieval. Two techniques, term relevance feedback and query suggestion, provide methods to help users formulate queries, but each is limited in different ways. In this research we combine these two techniques by automatically creating query suggestions using term relevance feedback techniques. To evaluate our approach, we conducted an interactive information retrieval study with 55 subjects and 20 topics. Each subject completed four topics, half with a term suggestion system and half with a query suggestion system. We also investigated the source of the suggestions: approximately half of all subjects were provided with system-generated suggestions, while half were provided with user-generated suggestions. Results show that subjects used more query suggestions than term suggestions and saved more documents with these suggestions, even though there were no significant differences in performance. Subjects preferred the query suggestion system and rated it higher along a number of dimensions including its ability to help them think of new approaches to searching. Qualitative data provided insight into subjects' usage and ratings, and indicated that subjects often used the suggestions even when they did not click on them.

References

[1]

Beaulieu, M.&Jones, S. (1998). Interactive searching and interface issues in the Okapi best match probabilistic retrieval system. Interacting with Computers, 10, 237--248.

[2]

Beerferman, D.,&Berger, A. (2000). Agglomerative clustering of a search engine query log. Proceedings ACM SIGKDD, Boston, MA, 407--416.

Digital Library

[3]

Belkin, N. J., Cool, C., Kelly, D., Lin, S. J., Park, S. Y., Perez-Carballo, J.,&Sikora, C. (2001). Iterative exploration, design and evaluation of support for query reformulation in interactive information retrieval. Information Processing&Management, 37(3), 404--434.

Digital Library

[4]

Billerbeck, B., Scholer, F., Williams, H. E.,&Zobel, J. (2003). Query expansion using associated queries. Proceedings CIKM '03, New Orleans, LA, 2--9.

Digital Library

[5]

Fitzpatrick, L.,&Dent, M. (1997). Automatic feedback using past queries: Social searching? Proceedings of SIGIR '97, Philadelphia, PA, 306--313.

Digital Library

[6]

Freyne, J., Farzan, R., Brusilovsky, P., Smyth, B.,&Coyle, M. (2007). Collecting community wisdom: Integrating social search&social navigation. Proceedings of IUI '07, Honolulu, HI, 52--61.

Digital Library

[7]

Hsieh-Yee, I. (1993).Effects of search experience and subject knowledge on online search behavior: Measuring the search tactics of novice and experienced searchers. Journal of the American Society for Information Science 44, 161--174.

[8]

Järvelin, K.&Kekääläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 20, 422--446.

Digital Library

[9]

Järvelin, K., Price, S. L, Delcambre, L. M. L.,&Nielsen, M. L. (2008). Discounted cumulated gain based evaluation of multiple-query IR sessions. Proceedings of ECIR '08, Glasgow, Scotland.

Digital Library

[10]

Joho, H., Coverson, C., Sanderson, M.,&Beaulieu, M. (2002). Hierarchical presentation of expansion terms. In Proceedings of the 17th Annual ACM Symposium on Applied Computing (SAC '02), Madrid, Spain, 645--649.

Digital Library

[11]

Jones, R., Rey, B., Madani, O.,&Greiner, W. (2006). Generating query substitutions. Proceedings of the WWW Conference, Edinburgh, Scotland, 387--396.

Digital Library

[12]

Kelly, D.,&Fu, X. (2006). Elicitation of term relevance feedback: An investigation of term source and context. Proceedings of SIGIR '06, 453--460.

Digital Library

[13]

Kelly, D., Gyllstrom, K.,&Bailey, E. W. (2009). Remote vs. face-to-face study mode: Differences in user behaviors, performance and feedback. SILS Tech. Report, 2009-02 (http://sils.unc.edu/research/techreports.html).

[14]

Mei, Q., Zhou, D.,&Church, K. (2008). Query suggestion using hitting time. Proceedings CIKM, Napa Valley, CA, 469--477.

Digital Library

[15]

Raghavan, V. V.,&Sever, H. (1995). On the reuse of past optimal queries. Proceedings of SIGIR '95, Seattle, WA, 344-350.

Digital Library

[16]

Ruthven, I. (2003). Re-examining the potential effectiveness of interactive query expansion. Proceedings of SIGIR '03, Toronto, CA, 213--220.

Digital Library

[17]

Ruthven, I.,&Lalmas, M. (2003). A survey on the use of relevance feedback for information access systems. Knowledge Engineering Review, 18(2), 95--145.

Digital Library

[18]

Smyth, B., Balfe, E., Freyne, J., Briggs, P., Coyle, M.,&Boydell, O. (2004). Exploiting query repetition and regularity in an adaptive community-based Web search Engine. User Modeling and User-Adapted Interaction, 14(5), 382--423.

Digital Library

[19]

White, R. W., Bilenko, M.,&Cucerzan, S. (2007). Studying the use of popular destinations to enhance web search interaction. Proceedings of SIGIR '07, Amsterdam, The Netherlands, 159--166.

Digital Library

[20]

White, R. W.&Marchionini, G. (2007). Examining the effectiveness of real-time query expansion. Information Processing&Management, 43, 685--704.

Digital Library

[21]

Vakkari, P. (2004). Changes in search tactics and relevance judgments when preparing a research proposal: A summary of the findings of a longitudinal study. Information Retrieval, 4(3--4), 295--310.

Digital Library

[22]

Voorhees, E. M. (2006). Overview of the TREC 2005 Robust Retrieval Track. Proceedings of TREC-14.

Cited By

Wang ZAi Q(2022)Simulating and Modeling the Risk of Conversational SearchACM Transactions on Information Systems10.1145/350735740:4(1-33)Online publication date: 24-Mar-2022
https://dl.acm.org/doi/10.1145/3507357
Hagen MArora PGhosh RThomas DJoshi S(2021)Class-Based Order-Independent Models of Natural Language for Bayesian Auto-Complete InferenceProceedings of the First International Conference on AI-ML Systems10.1145/3486001.3486240(1-7)Online publication date: 21-Oct-2021
https://dl.acm.org/doi/10.1145/3486001.3486240
Gao ZJiang JDemartini GZuccon GCulpepper JHuang ZTong H(2021)Evaluating Human-AI Hybrid Conversational Systems with Chatbot Message SuggestionsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482340(534-544)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482340
Show More Cited By

Index Terms

A comparison of query and term suggestion features for interactive searching
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Recommendations

Visual query suggestion: Towards capturing user intent in internet image search

Query suggestion is an effective approach to bridge the Intention Gap between the users' search intents and queries. Most existing search engines are able to automatically suggest a list of textual query terms based on users' current query input, which ...
Personalized Query Suggestion Diversification
SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Query suggestions help users refine their queries after they input an initial query. We consider the task of generating query suggestions that are personalized and diversified. We propose a personalized query suggestion diversification model (PQSD), ...
Post-ranking query suggestion by diversifying search results
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Query suggestion refers to the process of suggesting related queries to search engine users. Most existing researches have focused on improving the relevance of suggested queries. In this paper, we introduce the concept of diversifying the content of ...

Comments

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

86
Total Citations
View Citations
1,683
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)3

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wang ZAi Q(2022)Simulating and Modeling the Risk of Conversational SearchACM Transactions on Information Systems10.1145/350735740:4(1-33)Online publication date: 24-Mar-2022
https://dl.acm.org/doi/10.1145/3507357
Hagen MArora PGhosh RThomas DJoshi S(2021)Class-Based Order-Independent Models of Natural Language for Bayesian Auto-Complete InferenceProceedings of the First International Conference on AI-ML Systems10.1145/3486001.3486240(1-7)Online publication date: 21-Oct-2021
https://dl.acm.org/doi/10.1145/3486001.3486240
Gao ZJiang JDemartini GZuccon GCulpepper JHuang ZTong H(2021)Evaluating Human-AI Hybrid Conversational Systems with Chatbot Message SuggestionsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482340(534-544)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482340
Wang ZAi Q(2021)Controlling the Risk of Conversational Search via Reinforcement LearningProceedings of the Web Conference 202110.1145/3442381.3449893(1968-1977)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3449893
Palani SDing ZNguyen AChuang AMacNeil SDow SKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)CoNotate: Suggesting Queries Based on Notes Promotes Knowledge DiscoveryProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445618(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445618
Medlar ALi JGłowacka DScholer FThomas PElsweiler DJoho HKando NSmith C(2021)Query Suggestions as Summarization in Exploratory SearchProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446020(119-128)Online publication date: 14-Mar-2021
https://dl.acm.org/doi/10.1145/3406522.3446020
Jiang JAhuja NHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Response Quality in Human-Chatbot Collaborative SystemsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401234(1545-1548)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401234
Kangassalo LSpapé MJacucci GRuotsalo TPiwowarski BChevalier MGaussier EMaarek YNie JScholer F(2019)Why do Users Issue Good Queries?Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3331184.3331243(375-384)Online publication date: 18-Jul-2019
https://dl.acm.org/doi/10.1145/3331184.3331243
Fails JPera MAnuyah OKennington CWright KBigirimana WFails J(2019)Query Formulation Assistance for KidsProceedings of the 18th ACM International Conference on Interaction Design and Children10.1145/3311927.3323131(109-120)Online publication date: 12-Jun-2019
https://dl.acm.org/doi/10.1145/3311927.3323131
Ismail HBelkhouche BHarous S(2019)Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WikisIEEE Access10.1109/ACCESS.2019.29562847(172752-172773)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2956284
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.

Figures

Tables

Media

View Table of Conten