AKNOBAS: A Knowledge-based Segmentation Recommender System based on Intelligent Data Mining Techniques

Alejandro Rodríguez-González1, Javier Torres-Niño1, Enrique Jimenez-Domingo1, Juan Miguel Gomez-Berbis1 and Giner Alor-Hernandez2

  1. Computer Science Department, University Carlos III of Madrid
    28911, Leganes, Madrid, Spain
    {alejandro.rodriguez, javier.torres, enrique.jimenez, juanmiguel.gomez}@uc3m.es
  2. División de Estudios de Postgrado e Investigación, Instituto Tecnológico de Orizaba
    94320, Orizaba, Veracruz, México
    galor@itorizaba.edu.mx

Abstract

Recommender Systems have recently undergone an unwavering improvement in terms of efficiency and pervasiveness. They have become a source of competitive advantage in many companies which thrive on them as the technological core of their business model. In recent years, we have made substantial progress in those Recommender Systems outperforming the accuracy and added-value of their predecessors, by using cutting-edge techniques such as Data Mining and Segmentation. In this paper, we present AKNOBAS, a Knowledge-based Segmentation Recommender System, which follows that trend using Intelligent Clustering Techniques for Information Systems. The contribution of this Recommender System has been validated through a business scenario implementation proof-of-concept and provides a clear breakthrough of marshaling information through AI techniques.

Key words

Data Mining, Clustering, Information Systems, Artificial Intelligence, Use Case

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS110722008R

Publication information

Volume 9, Issue 2 (June 2012)
Year of Publication: 2012
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Rodríguez-González, A., Torres-Niño, J., Jimenez-Domingo, E., Gomez-Berbis, J. M., Alor-Hernandez, G.: AKNOBAS: A Knowledge-based Segmentation Recommender System based on Intelligent Data Mining Techniques. Computer Science and Information Systems, Vol. 9, No. 2, 713-740. (2012)