Design and implementation of an intelligent recommendation system for tourist attractions: The integration of EBM model, Bayesian network and Google Maps

Highlights? Based on the EBM model, we develop a decision support system for tourist attractions. ? The probability of a tourist attraction is calculated utilizing a Bayesian network. ? The accuracy of the prediction is validated by a ROC curve test. ? The recommended routes and tourist attractions are presented by Google Maps. Selecting tourist attractions and collecting related site information is one of the most crucial activities for a tourist when making decisions for a trip. Although various recommendation systems have been discussed over the last decade, rarely do such systems take individual tourist preference information into consideration. Based on the Engel-Blackwell-Miniard (EBM) model, this study used data published by the Tourism Bureau of Taiwan to develop a decision support system for tourist attractions. The probability of a tourist attraction appealing to a particular tourist is calculated utilizing a Bayesian network, and the accuracy of the prediction is validated by a ROC curve test. Finally, recommended routes and tourist attractions are presented through an interactive user interface using Google Maps. This study confirms that by combining the EBM model with a Bayesian network to propose a decision support system called the Intelligent Tourist Attractions System (ITAS). It has demonstrated good prediction of tourism attractions and provides useful map information to tourists.

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