An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles
In 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 gi...
- Autores:
-
silva d, jesus g
Vargas Villa, Jesús
Cabrera, Danelys
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_f744
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5128
- Acceso en línea:
- http://hdl.handle.net/11323/5128
https://repositorio.cuc.edu.co/
- Palabra clave:
- Information retrieval
Recommender systems
Topic modelling
- Rights
- openAccess
- License
- CC0 1.0 Universal
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|
dc.title.spa.fl_str_mv |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles |
title |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles |
spellingShingle |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles Information retrieval Recommender systems Topic modelling |
title_short |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles |
title_full |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles |
title_fullStr |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles |
title_full_unstemmed |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles |
title_sort |
An intelligent approach to design and development of personalized meta search: Recommendation of scientific articles |
dc.creator.fl_str_mv |
silva d, jesus g Vargas Villa, Jesús Cabrera, Danelys |
dc.contributor.author.spa.fl_str_mv |
silva d, jesus g Vargas Villa, Jesús Cabrera, Danelys |
dc.subject.spa.fl_str_mv |
Information retrieval Recommender systems Topic modelling |
topic |
Information retrieval Recommender systems Topic modelling |
description |
In 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. |
publishDate |
2019 |
dc.date.accessioned.none.fl_str_mv |
2019-07-31T22:41:39Z |
dc.date.available.none.fl_str_mv |
2019-07-31T22:41:39Z |
dc.date.issued.none.fl_str_mv |
2020 |
dc.type.spa.fl_str_mv |
Documento de Conferencia |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_f744 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/EC |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_f744 |
status_str |
acceptedVersion |
dc.identifier.uri.spa.fl_str_mv |
http://hdl.handle.net/11323/5128 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
url |
http://hdl.handle.net/11323/5128 https://repositorio.cuc.edu.co/ |
identifier_str_mv |
Corporación Universidad de la Costa REDICUC - Repositorio CUC |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.publisher.spa.fl_str_mv |
Universidad de la Costa |
institution |
Corporación Universidad de la Costa |
bitstream.url.fl_str_mv |
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silva d, jesus g1f6ba3473d23b4212c7f525ad6a550bcVargas Villa, Jesús8436c1dcc63d5be610be77cd256bb558Cabrera, Danelys1f9f790aa17165bd0e99ba6d950da3ee2019-07-31T22:41:39Z2019-07-31T22:41:39Z2020http://hdl.handle.net/11323/5128Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In 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. 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