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.
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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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Almomani, A., Saavedra, P., Sánchez, E. (2017). Ensembles of Decision Trees for Recommending Touristic Items. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_52
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DOI: https://doi.org/10.1007/978-3-319-59773-7_52
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