Técnicas Inteligentes en la gestión de la industria turística

Abstract

The tourism industry represents an opportunity for the development of different localities, thanks to the investments made in infrastructure and services, as well as the generation of employment, factors that drive economic and social growth. This industry has undergone rapid and profound transformations, achieving greater efficiency in the management of resources, optimizing planning and improving the operation of tourism services, all of this driven mainly by the adoption of new technologies. In this context, Machine Learning (ML) techniques are a promising resource for an industry that must innovate according to tourists' requirements. The integration of ML allows analyzing large datasets to adapt to changing market demands and offer more efficient services, thus boosting innovation and competitiveness of the tourism industry. This doctoral thesis addresses the study of ML techniques in the field of tourism management, addressed in three research articles that have been approved for publication in scientific journals indexed in Journal Citation Reports. 1. In the first article, Soft Computing techniques are used to analyze variables related to the operation of tourism companies in Ecuador, verifying the trend of the operation in different years and generating a valid source of information for decision making. In the study, dimensionality reduction techniques are applied to improve the interpretation, minimizing the loss of information. In addition, clustering techniques are applied to create groups according to the similarity of the characteristics and to provide a visual and numerical representation of the relationship of the data with each other. 2. The following study focuses on the review and synthesis of previously published research on Artificial Intelligence in the tourism sector. The study presents a categorization of the applications of Artificial Intelligence in different areas of tourism, recognizing valid studies and tools for the growth and innovation of the sector and highlighting the appropriation of Artificial Intelligence by the tourism industry. 3. The third paper focuses on the use of ML techniques to foresee hotel reservation cancellations. It discusses and implements key steps such as data preprocessing, hyperparameter settings, and model evaluation using performance metrics and graphs. The paper includes base classifiers, ensemble classifiers and neural networks. The studies analyzed in this thesis demonstrate the effectiveness of ML techniques to generate valuable information to support decision-making in tourism management. By analyzing the variables related to the operation of tourism companies, it is possible to identify the trend of the operation in different periods of time, also recognizing the effect of external factors. Also, from ML techniques it is possible to obtain highly accurate forecasting models, which are very useful in management to anticipate trends and optimize planning in the tourism sector. Likewise, an exhaustive review of the literature related to Artificial Intelligence in the tourism industry through applications, shows how these technologies transform the way of offering services, while enriching the user's experience, driving innovation and development in the sector. In short, these techniques are a very helpful resource to improve competitiveness levels in a constantly evolving market