MACHINE LEARNING IN TOURISM

A Special Issue in the International Journal of Machine Learning and Cybernetics
Springer
ISSN: 1868-8071
Impact factor: 4.377 (2021)
Editors-in-Chief: Xi-Zhao Wang, Daniel S. Yeung

We are witnessing unprecedented growth in the global tourism sector, even in spite of the severe restrictions imposed by Covid-19. This growth correlates with the advance of digital media and technological tools. Thus, the travelers' decision-making process, from searching for a suitable destination to posting comments on social media platforms, is conditioned by the proficiency of information and communication technologies. But technology not only influences travelers' behavior patterns but is also used by the different tourism stakeholders to improve the range of services and products they offer.

As the Internet is the main communication channel at all stages of the tourism process (especially using smartphones), companies have opted heavily for digitalization when dealing with their promotional processes, reservation management, personalized offers, etc., without losing sight of the challenges associated with this digitalization: security and trust. This digitalization has generated a large amount of tourism data that can be processed using advanced computational intelligence techniques for analytical and predictive purposes. In this area, many research opportunities are emerging, not only for the efficiency and cost-effectiveness of tourism businesses, but also for the user experience and for the governance of the sector in alignment with the 2030 Sustainable Development Goals, especially #9 (Industry, Innovation, and Infrastructure) and #11 (Sustainable Cities and Communities). The aim of this Special Issue is to present the state of the art on the emerging challenges and achievements regarding the use of machine learning, artificial intelligence, data science, data analytics, and big data, applied to the tourism context.

Submissions may fit into one of these subareas, among others:
(1) Tourism Planning: How to determine optimal tourism activities and services, such as the design of energy-efficient itineraries, economic landscapes and places of interest by considering environmental, meteorological, geographic, and seasonal, conditions.
(2) Tourism Forecasting: There are many variables to predict according to a variety of incoming factors.
(3) Tourism Recommendation: Recommender Systems make assumptions based on the preferences and behavior of the tourist. Recommendations allow service personalization for touristic companies.
(4) Tourism Prevention and Security: Machine learning and artificial intelligence methods give the power of foresight fraudulent activity through automated algorithms that work in patterns extracted and predicted from the data.

TOPICS OF INTEREST:

The topics of interest for this SI include, but are not limited to:
- Machine Learning
- Expert Systems
- Data Mining
- Big Data analysis
- Intelligent Systems
- Deep learning
- Recommender Systems
- Collaborative Filtering
- Forecasting
- Knowledge Analysis
- Optimization

SUBMISSIONS:

Open for submissions until December 31, 2023.
The format for the full article submission is available at: https://www.springer.com/journal/13042/submission-guidelines
Please, select the "S.I.: Machile Learning in Tourism" option while submitting the manuscript.

GUEST EDITORS:

Prof. Dr. Juan A. Gómez-Pulido (Lead Guest Editor)
Dep. Tech. of Computers and Communications, University of Extremadura (Spain)
Email: jangomez@unex.es

Assoc. Prof. Dr. Rafael Robina Ramírez
Dep. of Business, Universidad de Extremadura (Spain)
Email: rrobina@unex.es

Assoc. Prof. Jesús Torrecilla Piñero
Dep. of Engineering, Universidad de Extremadura (Spain)
Email: jtorreci@unex.es

Assoc. Prof. Dr. José Carlos Sancho Núñez
Dep. Computer Systems and Telematics Engineering, Universidad de Extremadura (Spain)
Email: jcsanchon@unex.es