TourMate: A Personalized Multi-factor Based Tourist Place Recommendation System Using Machine Learning

TourMate: A Personalized Multi-factor Based Tourist Place Recommendation System Using Machine Learning

Автор: Azmain Abid Khan, Mahfuzulhoq Chowdhury

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 4 vol.16, 2024 года.

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Building a personalized travel recommendation system is important to enhance the satisfaction and experience of travelers. Due to the lack of an efficient online-based tourist assistance system, tourists have faced several challenges in Bangladesh, such as difficulties in planning their trips and making informed decisions. To overcome the existing challenges, in this paper, a prediction model has been developed to predict the suitability of a travel destination based on the user’s preferences and some other relevant factors. Then the system offers personalized recommendations for the best local places to visit, hotels to stay in, transportation services, and travel agencies with the necessary details. This paper utilizes various machine learning classification algorithms to predict the best-suited travel destinations and local tourist spot recommendations for users based on their budget and preferences. The examined results verified that the random forest algorithm provides the best accuracy of 98 percent and is used for tourist place eligibility prediction. The user rating analysis visualized that the proposed mobile application received satisfactory remarks from more than 60 percent of reviewers regarding its effectiveness.

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Automated Recommendation System, Personalized Travel Recommendations, User Preferences, Machine Learning, Mobile Application, Tourist Place Recommendation

Короткий адрес: https://sciup.org/15019377

IDR: 15019377   |   DOI: 10.5815/ijisa.2024.04.04

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