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dc.contributor.authorsilva d, jesus gspa
dc.contributor.authorVargas Villa, Jesússpa
dc.contributor.authorCabrera, Danelysspa
dc.date.accessioned2019-07-31T22:41:39Z
dc.date.available2019-07-31T22:41:39Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/11323/5128spa
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.spa
dc.language.isoeng
dc.publisherUniversidad de la Costaspa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectInformation retrievalspa
dc.subjectRecommender systemsspa
dc.subjectTopic modellingspa
dc.titleAn intelligent approach to design and development of personalized meta search: Recommendation of scientific articlesspa
dc.typeDocumento de Conferenciaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.type.coarhttp://purl.org/coar/resource_type/c_f744spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/conferenceObjectspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ECspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


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