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Ensembles of Decision Trees for Recommending Touristic Items

  • Ameed Almomani
  • Paula Saavedra
  • Eduardo SánchezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10338)

Abstract

This article analyzes the performance of ensembles of decision trees when applied to the task of recommending tourist items. The motivation comes from the fact that there is an increasing need to explain why a website is recommending some items and not others. The combination of decision trees and ensemble learning is therefore a good way to provide both interpretability and accuracy performance. The results demonstrate the superior performance of ensembles when compared to single decision tree approaches. However, basic colaborative filtering methods seem to perform better than ensembles in our dataset. The study suggests that the number of available features is a key aspect in order to get the true potential of this type of ensembles.

Keywords

Ensembles Decision trees Recomendations Tourism 

Notes

Acknowledgments

This work has received financial support from the Ministry of Science and Innovation of Spain under grant TIN2014-56633-C3-1-R as well as from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08) and the European Regional Development Fund (ERDF).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ameed Almomani
    • 1
  • Paula Saavedra
    • 1
  • Eduardo Sánchez
    • 1
    Email author
  1. 1.Grupo de Sistemas Inteligentes (GSI), Centro Singular de Investigación en Tecnologías de la Información (CITIUS)Universidad de Santiago de CompostelaSantiago de CompostelaSpain

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