Analyzing Tourist Behavior in Virtual Museums Using Intelligent Approach with Feature Selection

The Thai tourism sector has been significantly influenced by recent advances in multimedia technologies that help promote tourism, with Virtual Reality being exemplary. This study analyzes and predicts the behavior of tourists visiting the Virtual Chaiya National Museum to develop standards of quality for its products and services. Virtual Reality is useful in attracting tourists as it applies the concept of gamification using multimedia approaches. A sample of 580 tourists was used, and machine learning techniques were employed to predict the tourists??? behavior. The results showed an accuracy of 99.48% using particle swarm optimization with random forest, followed by 99.45% using genetic algorithm search, and 99.13% using best-first search. Methods of feature selection were used with an Apriori algorithm to render the search for the rules governing tourist behavior more efficient. Particle swarm optimization with the Apriori algorithm yielded an effective confidence of 1.0. Tourist satisfaction with the quality of service at the Virtual Chaiya National Museum was 81.2%. The findings here can be used to choose the optimum dataset of features to create an effective prediction model and generate rules that can be used to describe tourist behavior.