Paper Title
Travel Route Recommendation and Sustainable Optimization User Preference Model
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.