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
This is a preview of subscription content, access via your institution.
Buying options
References
Ali, S., Tirumala, S.S., Sarrafzadeh, A.: Ensemble learning methods for decision making: status and future prospects. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 211–216. IEEE (2015)
Bar, A., Rokach, L., Shani, G., Shapira, B., Schclar, A.: Improving simple collaborative filtering models using ensemble methods. In: Zhou, Z.-H., Roli, F., Kittler, J. (eds.) MCS 2013. LNCS, vol. 7872, pp. 1–12. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38067-9_1
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)
Erdal, H.I., Karakurt, O.: Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms. J. Hydrol. 477, 119–128 (2013). Elsevier
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics. Springer-Verlag New York, New York (2009)
Ghimire, B., Rogan, J., Galiano, V.R., Panday, P., Neeti, N.: An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GIScience Remote Sens. 49(5), 623–643 (2012). Taylor & Francis
Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 595–604. ACM (2011)
Lavanya, D., Rani, K.U.: Ensemble decision making system for breast cancer data. Int. J. Comput. Appl. 51(17) (2012). Foundation of Computer Science
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006). IEEE
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Saavedra, P., Barreiro, P., Durán, R., Crujeiras, R., Loureiro, M., Sánchez, V.E.: Choice-based recommender systems. In: Proceedings of RecSys 2016, Boston (2016)
Utku, A., Hacer, K., Yildiz, O., Akcayol, M.A.: Implementation of a new recommendation system based on decision tree using implicit relevance feedback. JSW 10(12), 1367–1374 (2015)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-59773-7_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59772-0
Online ISBN: 978-3-319-59773-7
eBook Packages: Computer ScienceComputer Science (R0)