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dc.date.accessioned 2012-11-15T16:19:34Z
dc.date.available 2012-11-15T16:19:34Z
dc.date.issued 2006-08
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/24245
dc.description.abstract 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. en
dc.language en es
dc.subject Query formulation es
dc.subject click-through data en
dc.title Automatic query recommendation using click-through data en
dc.type Objeto de conferencia es
sedici.identifier.isbn 0-387-34655-4 es
sedici.creator.person Dupret, George es
sedici.creator.person Mendoza, Marcelo es
sedici.description.note Applications in Artificial Intelligence - Applications es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Red de Universidades con Carreras en Informática (RedUNCI) es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
sedici.date.exposure 2006-08
sedici.relation.event 19 th IFIP World Computer Congress - WCC 2006 es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)