Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing

Recommendation & mobile systems - a state of the art for tourism

Recommendation systems have been growing in number over the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. These approaches contain strengths and weaknesses that need to be evaluated according to the knowledge area in which the system is going to be implemented. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages

"Applications of Intelligent Systems in Tourism: Relevant Methods"

"This article presents a literature review of Intelligent Systems applications in Tourism in different parts of the world. The review focuses on the most relevant methods in free and paid databases, in English and Spanish. Most of the works deal with methodologies based on artificial intelligence, such as expert systems, fuzzy logic, machine learning, data mining, neural networks, genetic algorithms. In the review, several characteristics of the systems have been taken into account, such as, knowledge base, actors in the operation of the system, types of tourists, usefulness or not in space and time. According to the review it was found that most of the researches are deterministic models, the most used methodology in tourism are the expert systems based on rules, observing an emerging innovation in metaheuristics, mainly genetic algorithms and intelligent systems with knowledge base based on optimization methods for route choice or optimal visit plan. It is expected that this work serves to give a general perspective on the application of intelligent systems in the area of tourism, as well as a work that consolidates background for work in this area of research.

IoT in Arctic Tourism – A Case-study of the Tourist Information Office in Narvik

Source at: https://fruct.org/publications/volume-32/acm32/</a

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Personalized And Situation-Aware Recommendations For Runners

The project uService investigates the transformation of a mobile user into a service super prosumer, i.e., a producer, provider and consumer of services at the same time. The goal is to develop a platform which enables a user to create, discover and consume mobile services anywhere and at any time on the mobile device. uRun is an application scenario of the project in the field of mobile health and fitness. The uRun framework provides a mobile assistance system particularly for runners, which combines Web 2.0 and Web 3.0 technologies and personalized and situation-aware recommendation mechanisms. The ability to create individual and mobile health and fitness services as well as a personalized and situation-aware assistance system based on a semantic knowledge base are considered to provide an edge over existing consumer-centric health care systems. In this article, we describe the recommendation mechanism and the incorporation of semantic knowledge for the uService platform and the uRun framework

Artificial Intelligence in Smart Tourism: A Conceptual Framework

Smart tourism destination as: an innovative tourist destination, built on an infrastructure of state-of-the-art technology guaranteeing the sustainable development of tourist areas, accessible to everyone, which facilitates the visitor’s interaction with and integration into his or her surroundings, increases the quality of the experience at the destination, and improves residents’ quality of life. Lopez de Avila (2015). Smart tourism involves multiple components and layers of “smart” include (1) Smart Destinations which was special cases of smart cities integration of ICT’s into physical infrastructure, (2) Smart experience which specifically focus on technology-mediated tourism experience and their engagement through personalization, context-awareness and real-time monitoring, (3) Smart business refer to the complex business ecosystem that creates and supports the exchange of touristic resource and the co-creation of tourism experience. Gretzel et al, (2015). Smart tourism also clearly relies on the ability to not only collect enormous of data but to intelligently store, process, combine, analyze and use big data to inform business innovation, operations and services by artificial intelligence and big data technique. The rapid development of information communication technology (ICT) such as artificial intelligent, cloud computing, mobile device, big data mining and social media cause computing, storage and communication relevant software and hardware popular. Facebook, Amazon, Apple, Microsoft and Google have risen rapidly since 2000. In recent years, Emerging technologies such as Artificial Intelligence, Internet of Thing, Robotic, Cyber Security, 3D printer and Block chain also accelerate the development of industry toward digital transformation trend such as Fintech, e-commerce, smart cities, smart tourism, smart healthcare, smart manufacturing... This study proposes a conceptual framework that integrates (1) artificial intelligence/machine learning, (2) institution/organizational and (3) business processes to assist smart tourism stake holder to leverage artificial intelligence to integrate cross-departmental business and streamline key performance metrics to build a business-level IT Strategy. Artificial intelligence as long as the function includes (1) Cognitive engagement to (voice/pattern recognition function) (2) Cognitive process automation (Robotic Process Automation) (3) Cognitive insight (forecast, recommendation)