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dc.creatorsilva d, jesus g
dc.creatorVargas Villa, Jesús
dc.creatorCabrera, Danelys
dc.date.accessioned2019-07-31T22:41:39Z
dc.date.available2019-07-31T22:41:39Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11323/5128
dc.description.abstractIn this article we present a method to recommend articles scientists taking into account their degree of generality or specificity. In terms of methodology, two approaches are presented to recommend articles based on Topic Modeling. The first of these is based on the divergence of topics that are given in the documents, while the second is based on the similarity between these topics. After a validation process it was demonstrated that the proposed methods are more efficient than the traditional methods.es_ES
dc.language.isoenges_ES
dc.publisherUniversidad de la Costaes_ES
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectInformation retrievales_ES
dc.subjectRecommender systemses_ES
dc.subjectTopic modellinges_ES
dc.titleAn intelligent approach to design and development of personalized meta search: Recommendation of scientific articleses_ES
dc.typeConference paperes_ES
dc.type.hasVersioninfo:eu-repo/semantics/draftes_ES
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses_ES


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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal