Automatic query recommendation using click-through data

Autores
Dupret, George; Mendoza, Marcelo
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
We present a method to help a user rede ne a query suggesting a list of similar queries. The method proposed is based on clickthrough data were sets of similar queries could be identi ed. Scienti c literature shows that similar queries are useful for the identi cation of di erent information needs behind a query. Unlike most previous work, in this paper we are focused on the discovery of better queries rather than related queries. We will show with experiments over real data that the identi cation of better queries is useful for query disambiguation and query specialization.
Applications in Artificial Intelligence - Applications
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Query formulation
click-through data
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/24245

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network_name_str SEDICI (UNLP)
spelling Automatic query recommendation using click-through dataDupret, GeorgeMendoza, MarceloCiencias InformáticasQuery formulationclick-through dataWe present a method to help a user rede ne a query suggesting a list of similar queries. The method proposed is based on clickthrough data were sets of similar queries could be identi ed. Scienti c literature shows that similar queries are useful for the identi cation of di erent information needs behind a query. Unlike most previous work, in this paper we are focused on the discovery of better queries rather than related queries. We will show with experiments over real data that the identi cation of better queries is useful for query disambiguation and query specialization.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/24245enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34655-4info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:46Zoai:sedici.unlp.edu.ar:10915/24245Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:46.377SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Automatic query recommendation using click-through data
title Automatic query recommendation using click-through data
spellingShingle Automatic query recommendation using click-through data
Dupret, George
Ciencias Informáticas
Query formulation
click-through data
title_short Automatic query recommendation using click-through data
title_full Automatic query recommendation using click-through data
title_fullStr Automatic query recommendation using click-through data
title_full_unstemmed Automatic query recommendation using click-through data
title_sort Automatic query recommendation using click-through data
dc.creator.none.fl_str_mv Dupret, George
Mendoza, Marcelo
author Dupret, George
author_facet Dupret, George
Mendoza, Marcelo
author_role author
author2 Mendoza, Marcelo
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Query formulation
click-through data
topic Ciencias Informáticas
Query formulation
click-through data
dc.description.none.fl_txt_mv We present a method to help a user rede ne a query suggesting a list of similar queries. The method proposed is based on clickthrough data were sets of similar queries could be identi ed. Scienti c literature shows that similar queries are useful for the identi cation of di erent information needs behind a query. Unlike most previous work, in this paper we are focused on the discovery of better queries rather than related queries. We will show with experiments over real data that the identi cation of better queries is useful for query disambiguation and query specialization.
Applications in Artificial Intelligence - Applications
Red de Universidades con Carreras en Informática (RedUNCI)
description We present a method to help a user rede ne a query suggesting a list of similar queries. The method proposed is based on clickthrough data were sets of similar queries could be identi ed. Scienti c literature shows that similar queries are useful for the identi cation of di erent information needs behind a query. Unlike most previous work, in this paper we are focused on the discovery of better queries rather than related queries. We will show with experiments over real data that the identi cation of better queries is useful for query disambiguation and query specialization.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
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dc.language.none.fl_str_mv eng
language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
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