AKNOBAS: A Knowledge-based Segmentation Recommender System based on Intelligent Data Mining Techniques
Computer Science and Information Systems, Tome 9 (2012) no. 2.

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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.
Keywords: Data Mining, Clustering, Information Systems, Artificial Intelligence, Use Case
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     author = {Alejandro Rodr{\'\i}guez-Gonz\'alez and Javier Torres-Ni\~no and Enrique Jimenez-Domingo and Juan Miguel Gomez-Berbis and Giner Alor-Hernandez},
     title = {AKNOBAS: {A} {Knowledge-based} {Segmentation} {Recommender} {System} based on {Intelligent} {Data} {Mining} {Techniques}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {9},
     number = {2},
     year = {2012},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2012_9_2_a11/}
}
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Alejandro Rodríguez-González; Javier Torres-Niño; Enrique Jimenez-Domingo; Juan Miguel Gomez-Berbis; Giner Alor-Hernandez. AKNOBAS: A Knowledge-based Segmentation Recommender System based on Intelligent Data Mining Techniques. Computer Science and Information Systems, Tome 9 (2012) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2012_9_2_a11/