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Machine Learning Mechanism for Adaptive Tourist Recommendation Using Bayesian Algorithm

Pijitra Jomsri, Worasit Choochaiwattana


Currently, recommender systems are available in many daily activities such as online shopping search, and social networks. Due to the increasing demand of the tourism industry through information technology, the recommender systems are integrated into the tourism website. This research aims at exploiting user data to recommend tourist attractions by arranging the attractions together with tourism-related information and making recommendations based on information relevant to the needs of each user. The proposed mechanism has an advantage as it can suggest information at the beginning of use without the need for usage history, rankings, and other special knowledge. Thus, new travelers can get recommendations when start using the recommender system. This research focuses on recommending tourist destinations in Thailand using machine learning methods based on Bayesian Personalized Ranking to predict tourist attraction rankings by comparing four methods: 1) Collaborative Filtering Only, 2) Demographic Filtering Only, 3) Collaborative Filtering and Demographic Filtering, and 4) Hybrid Method of Demographic Filtering and Demographic Filtering Combining with Tourist Attraction Category. The experimental results show that the hybrid method of collaborative filtering and demographic filtering combining with the ranking of tourist attractions recommends tourist attractions better than other methods. Therefore, this hybrid model can be used as a model to support the Recommender system of tourism.


Machine Learning, Bayesian Personalized Ranking, Tourist Recommendation, Recommender System.

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