Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Abstract

Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as ''Turkey Tourism Year'' in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.76. Conclusions: It was observed that it is possible to find the tourists who will prefer Turkey by using the developed system. In other words, the study revealed that the countries can reach the individual instead of masses in their promotional activities.

Autoencoder-Enabled Potential Buyer Identification and Purchase Intention Model of Vacation Homes

“…Both direct and indirect effects of tourists' destination familiarity and enduring involvement on place attachment and purchase intentions of vacation homes were examined. Based on the enduring involvement theory [17], the place attachment theory [12], and the destination familiarity theory [18], [20], a theoretical model was designed in Section III-A- (2).…”

“…In Western countries, enjoying regular trips to vacation homes, such as weekend villas or holiday apartments in tourist destinations, has become an integral form of leisure [1]. Similarly, in China, with the rapid development of tourism, enjoying long-term vacations has become common [2], [3]. For example, tourists flock from the widespread smog and chilly weather in many northern cities of China to southern cities…”

Trip Planner: A Big Data Analytics Based Recommendation System for Tourism Planning

“…S. M. Al-Ghuribiet al [24] have proposed a multi-criteria recommender system (MCRSs) to improve accuracy of Recommender System(RS) performance by incorporating review elements relevant to entire system or the specific feature. I. Topal et al [25] have applied artificial intelligence to analyse the data from TripAdvisor on the basis of travel history as well as travel preferences. As a result of their work, travel potential for Turkey was identified and 2018 was declared as "Turkey Tourism Year" in China.…”