Paper Title
TRAVEL ROUTE RECOMMENDATION AND SUSTAINABLE OPTIMIZATION USER PREFERENCE MODEL

Abstract
Abstract - We design and develop a travel route recommendation system based on a sustainable optimization user preference model. The system can learn and store the user's travel preferences, and when the user travels to different cities, the system can quickly provide the user with travel route suggestions based on the user's preference data. We propose a sustainable optimization user preference model to record users' travel preferences. In this user preference model, we classify users' travel preferences into short-term preferences for tourist attraction types and long-term preferences for attraction attributes. Based on the user preference model and the user's demand for travel, we develop a travel route generation algorithm to generate personalized travel routes. The system interacts with the user while recommending travel routes and continuously optimizes the user preference model based on the user's feedback. Because the user preference model can be continuously optimized, the system can adaptively match the user's new travel preferences even if the user's preferences change. We showed the system to 26 students at Yamaguchi University and conducted a questionnaire survey. The survey results indicated that more than 70% of the respondents felt that the travel routes generated by the system could match the user's travel preferences. In the comparison experiment, more than 64% of the respondents thought that the travel routes generated by the system were better. Keywords - Travel route, Recommendation System, Preference Model.