Information technology for choosing routes using machine learning methods

DOI: 10.31673/2412-4338.2022.024452

Abstract

Information technologies penetrate into all spheres of human life and the activity of companies. The sphere of tourism did not escape their influence. Tourism is a leisure activity that involves complex decision-making processes, which is why the development of recommendation systems that will help facilitate these processes is relevant. It is for this purpose that the method of selecting tourist routes using machine learning methods was created in the work. In the course of the research, the field of tourism was analyzed, the peculiarities of recommendation systems were established. The levels of the proposed methodology are described, indicating the features of the backend and frontend. A characteristic feature of the proposed methodology is the use of machine learning classifiers, such as decision trees, support vector method, and multilayer perceptron, in the model of optimal selection. It is this combination of methods that will allow you to choose the best route from the proposed ones. They are used to distinguish specific destinations within each data set. To make the complex model usable and interpret its results for the tourist, the decision tree models are transformed into decision rules, and then the information is passed to the user interface control module.

In order to obtain a high-quality forecast, the methods of machine learning were considered in detail, and their features were determined. It has been found that the classification performance can be improved by using a combination of classifiers and methods such as decision trees, support vector method and multilayer perceptron. This combination of algorithms will allow to predict the optimal tourist route. Both technical and practical aspects, including data sparsity, scalability, transparency, system accuracy, usability, and user acceptability, were considered to evaluate the effectiveness of the proposed methodology.

Keywords: information technology, tourism, machine learning, recommender systems, decision tree, support vector method, multilayer perceptron.

References:

1. Christopher M Bishop. Pattern recognition. Machine Learning, 2006, 128 p.
2. Ethem Alpaydin. Introduction To Machine Learning, 2009, 584 p.
3. Garcia I., Sebastia L., Onaindia E. On the design of individual and group recommender systems for tourism. Expert Systems with Applications 38 - 2011. 7683–7692. https://doi.org/10.1016/j.eswa.2010.12.143
4. Gavalas D., Kenteris M., Konstantopoulos C., Pantziou G. Web application for recommending personalised mobile tourist routes. IET Software 6 - 2012. 313–322. https://doi.org/10.1049/iet-sen.2011.0156
5. Isinkaye F.O., Folajimi Y.O., Ojokoh B.A. Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal 16 -2015. 261–273. https://doi.org/10.1016/j.eij.2015.06.005
6. Kantamneni A., Brown L.E., Parker G., Weaver W.W. Survey of multi-agent systems for microgrid control. Engineering Applications of Artificial Intelligence 45 - 2015. 192–203. https://doi.org/10.1016/j.engappai.2015.07.005
7. Saleh E., Błaszczyński J., Moreno A., Valls A., Romero-Aroca P., de la Riva-Fernández S., Słowiński R. Learning ensemble classifiers for diabetic retinopathy assessment. Artificial Intelligence in Medicine - 2017. https://doi.org/10.1016/j.artmed.2017.09.006
8. Tom M. Mitchell. Machine Learning [http://www.cs.cmu.edu/~tom/mlbook.html]
9. Yeh, D.-Y., Cheng, C.-H. Recommendation system for popular tourist attractions in Taiwan using Delphi panel and repertory grid techniques. Tourism Management 46 - 2015. 164–176. https://doi.org/10.1016/j.tourman.2014.07.002