research-article

Learning by Example: Training Users with High-quality Query Suggestions

Authors:

David ElsweilerAuthors Info & Claims

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 133 - 142

https://doi.org/10.1145/2766462.2767731

Published: 09 August 2015 Publication History

Abstract

The queries submitted by users to search engines often poorly describe their information needs and represent a potential bottleneck in the system. In this paper we investigate to what extent it is possible to aid users in learning how to formulate better queries by providing examples of high-quality queries interactively during a number of search sessions. By means of several controlled user studies we collect quantitative and qualitative evidence that shows: (1) study participants are able to identify and abstract qualities of queries that make them highly effective, (2) after seeing high-quality example queries participants are able to themselves create queries that are highly effective, and, (3) those queries look similar to expert queries as defined in the literature. We conclude by discussing what the findings mean in the context of the design of interactive search systems.

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Cited By

Lindstrom HCorona Pampin HPalumbo ELiu A(2024)Encouraging Exploration in Spotify Search through Query RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688035(775-777)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688035
Liu ACorona Pampin HPalumbo EChen HDuh WHuang HKato MMothe JPoblete B(2023)Bootstrapping Query Suggestions in Spotify's Instant Search SystemProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591827(3230-3234)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591827
Wang BLiu J(2023)Characterizing and Early Predicting User Performance for Adaptive Search Path RecommendationProceedings of the Association for Information Science and Technology10.1002/pra2.79960:1(408-420)Online publication date: 22-Oct-2023
https://doi.org/10.1002/pra2.799
Show More Cited By

Index Terms

Learning by Example: Training Users with High-quality Query Suggestions
1. Information systems
  1. Information retrieval

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Published In

SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

August 2015

1198 pages

ISBN:9781450336215

DOI:10.1145/2766462

General Chair:
Ricardo Baeza-Yates
Yahoo Labs, USA
,
Program Chairs:
Mounia Lalmas
Yahoo Labs, UK
,
Alistair Moffat
University of Melbourne, Australia
,
Berthier Ribeiro-Neto
Google, Brazil, and UFMG, Brazil

Copyright © 2015 ACM.

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Published: 09 August 2015

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SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval

August 9 - 13, 2015

Santiago, Chile

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SIGIR '15 Paper Acceptance Rate 70 of 351 submissions, 20%;

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

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Cited By

Lindstrom HCorona Pampin HPalumbo ELiu A(2024)Encouraging Exploration in Spotify Search through Query RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688035(775-777)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688035
Liu ACorona Pampin HPalumbo EChen HDuh WHuang HKato MMothe JPoblete B(2023)Bootstrapping Query Suggestions in Spotify's Instant Search SystemProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591827(3230-3234)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591827
Wang BLiu J(2023)Characterizing and Early Predicting User Performance for Adaptive Search Path RecommendationProceedings of the Association for Information Science and Technology10.1002/pra2.79960:1(408-420)Online publication date: 22-Oct-2023
https://doi.org/10.1002/pra2.799
Kotake JKitayama DAtzenbeck CRubart J(2022)Search UI with fill-in-the-blank for clarifying purpose of information exploration and its evaluationProceedings of the 5th Workshop on Human Factors in Hypertext10.1145/3538882.3542794(1-8)Online publication date: 28-Jun-2022
https://dl.acm.org/doi/10.1145/3538882.3542794
Ihoriya HSuzuki MYamamoto Y(2022)Mitigating Position Bias in Review Search Results with Aspect Indicator for Loss AversionHuman Interface and the Management of Information: Applications in Complex Technological Environments10.1007/978-3-031-06509-5_2(17-32)Online publication date: 16-Jun-2022
https://doi.org/10.1007/978-3-031-06509-5_2
Demelo JSedig K(2021)Interfaces for Searching and Triaging Large Document Sets: An Ontology-Supported Visual Analytics ApproachInformation10.3390/info1301000813:1(8)Online publication date: 27-Dec-2021
https://doi.org/10.3390/info13010008
Demelo JSedig K(2021)Design of Generalized Search Interfaces for Health InformaticsInformation10.3390/info1208031712:8(317)Online publication date: 6-Aug-2021
https://doi.org/10.3390/info12080317
Ortloff AZimmerman SElsweiler DHenze NScholer FThomas PElsweiler DJoho HKando NSmith C(2021)The Effect of Nudges and Boosts on Browsing Privacy in a Naturalistic EnvironmentProceedings of the 2021 Conference on Human Information Interaction and Retrieval10.1145/3406522.3446014(63-73)Online publication date: 14-Mar-2021
https://dl.acm.org/doi/10.1145/3406522.3446014
Losada DElsweiler DHarvey MTrattner C(2021)A day at the racesApplied Intelligence10.1007/s10489-021-02719-2Online publication date: 17-Aug-2021
https://doi.org/10.1007/s10489-021-02719-2
Umemoto KYamamoto TTanaka K(2020)Search Support ToolsUnderstanding and Improving Information Search10.1007/978-3-030-38825-6_8(139-160)Online publication date: 30-May-2020
https://doi.org/10.1007/978-3-030-38825-6_8
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