research-article

Automatically identifying localizable queries

Authors:

Michael J. Welch,

Junghoo ChoAuthors Info & Claims

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 507 - 514

https://doi.org/10.1145/1390334.1390421

Published: 20 July 2008 Publication History

Abstract

Personalization of web search results as a technique for improving user satisfaction has received notable attention in the research community over the past decade. Much of this work focuses on modeling and establishing a profile for each user to aid in personalization. Our work takes a more query-centric approach. In this paper, we present a method for efficient, automatic identification of a class of queries we define as localizable from a web search engine query log. We determine a set of relevant features and use conventional machine learning techniques to classify queries. Our experiments find that our technique is able to identify localizable queries with 94% accuracy.

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Cited By

Gupta MJoshi MAgrawal P(2023)Deep Learning Methods for Query Auto CompletionAdvances in Information Retrieval10.1007/978-3-031-28241-6_35(341-348)Online publication date: 16-Mar-2023
https://doi.org/10.1007/978-3-031-28241-6_35
Guo JLan Y(2020)Query ClassificationQuery Understanding for Search Engines10.1007/978-3-030-58334-7_2(15-41)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_2
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Show More Cited By

Index Terms

Automatically identifying localizable queries
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Published In

cover image ACM Conferences

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

Copyright © 2008 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]

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Publication History

Published: 20 July 2008

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SIGIR '08

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SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

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

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Cited By

Gupta MJoshi MAgrawal P(2023)Deep Learning Methods for Query Auto CompletionAdvances in Information Retrieval10.1007/978-3-031-28241-6_35(341-348)Online publication date: 16-Mar-2023
https://doi.org/10.1007/978-3-031-28241-6_35
Guo JLan Y(2020)Query ClassificationQuery Understanding for Search Engines10.1007/978-3-030-58334-7_2(15-41)Online publication date: 2-Dec-2020
https://doi.org/10.1007/978-3-030-58334-7_2
Liu JLiu CBelkin N(2020)Personalization in text information retrievalJournal of the Association for Information Science and Technology10.1002/asi.2423471:3(349-369)Online publication date: 28-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24234
Massai LNesi PPantaleo G(2019)PAVAL: A location-aware virtual personal assistant for retrieving geolocated points of interest and location-based servicesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2018.09.01377(70-85)Online publication date: Jan-2019
https://doi.org/10.1016/j.engappai.2018.09.013
Fourney ADumais SPerego RSebastiani FAslam JRuthven IZobel J(2016)Automatic Identification and Contextual Reformulation of Implicit System-Related QueriesProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2914701(761-764)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2914701
Missaoui SFaiz R(2015)A Preferences Based Approach for Better Comprehension of User Information NeedsTransactions on Computational Collective Intelligence XVIII10.1007/978-3-662-48145-5_4(67-85)Online publication date: 31-Jul-2015
https://doi.org/10.1007/978-3-662-48145-5_4
Fang YBawab ZCrespo J(2014)Collaborative Language Models for Localized Query PredictionACM Transactions on Interactive Intelligent Systems10.1145/26226174:2(1-21)Online publication date: 1-Jun-2014
https://dl.acm.org/doi/10.1145/2622617
Missaoui SFaiz R(2014)A Preferences Based Approach for Better Comprehension of User Information NeedsComputational Collective Intelligence. Technologies and Applications10.1007/978-3-319-11289-3_10(94-103)Online publication date: 2014
https://doi.org/10.1007/978-3-319-11289-3_10
Missaoui SFaiz R(2014)A New Preference Based Model for Relevant Dimension Identification in Contextual Mobile SearchNetworked Systems10.1007/978-3-319-09581-3_15(215-229)Online publication date: 3-Aug-2014
https://doi.org/10.1007/978-3-319-09581-3_15
Missaoui SFaiz R(2014)Adapting User’s Context to Understand Mobile Information NeedsModern Trends and Techniques in Computer Science10.1007/978-3-319-06740-7_29(343-354)Online publication date: 6-May-2014
https://doi.org/10.1007/978-3-319-06740-7_29
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