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
| Summary: | 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. |
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