Revistas de investigación UNMSM

RECOMMENDER SYSTEMS: A FOCUS FROM THE GENETIC ALGORITHMS

Authors

  • Oswaldo Velez-Langs Universidad del Sinú
  • Carlos Santos Universidad Rey Juan Carlos

DOI:

https://doi.org/10.15381/idata.v9i1.5743

Keywords:

Collaborative information filtering, machine learning, evolutionary algorithms, adaptive user interfaces.

Abstract

This work presents an alternative approach (Evolutionary Algorithms approach) to traditional treatment of Recommender Systems (RSs). The work examines genetic algorithms possibilities to offer adaptive characteristics to this systems trough learning. The main goal, in addition to give a general view about RSs capabilities and possibilities, it is to provide an example mechanism for to extend RSs learning capabilities (from users´s personal chracteristics), with the purpose to improve the effectiveness in the moment of to find recommendations and appropriate suggestions for particular individuals.

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Author Biographies

  • Oswaldo Velez-Langs, Universidad del Sinú
    Investigador del Departamento de Informática, Estadística y Telemática, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos (España). Profesor de la Facultad de Ingenierías, Universidad del Sinú (Colombia).
  • Carlos Santos, Universidad Rey Juan Carlos
    Profesor investigador del Departamento de Arquitectura, Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Universidad Rey Juan Carlos (España).

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Published

2006-07-31

Issue

Vol. 9 No. 1 (2006)

Section

Producción y Gestión

License

Copyright (c) 2006 Oswaldo Velez-Langs, Carlos Santos

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

AUTHORS RETAIN THEIR RIGHTS:

a. Authors retain their trade mark rights and patent, and also on any process or procedure described in the article.

b. Authors retain their right to share, copy, distribute, perform and publicly communicate their article (eg, to place their article in an institutional repository or publish it in a book), with an acknowledgment of its initial publication in the INDUSTRIAL DATA.

c. Authors retain theirs right to make a subsequent publication of their work, to use the article or any part thereof (eg a compilation of his papers, lecture notes, thesis, or a book), always indicating the source of publication (the originator of the work, journal, volume, number and date).

How to Cite

RECOMMENDER SYSTEMS: A FOCUS FROM THE GENETIC ALGORITHMS. (2006). Industrial Data, 9(1), 023-031. https://doi.org/10.15381/idata.v9i1.5743