Simultaneous Clustering of Sightseeing Spots and Visitors Based on Real Trajectory Data and Extracting Personalized Secret Spots

This research aims to cluster sightseeing spots and visitors using real trajectory data while also identifying personalized secret spots for individuals. With the proliferation of information technology like the Internet and social media, tourists now have more options for visiting spots that suit their interests. The clustering approach used in this study can pair groups of sightseeing spots with groups of tourists, thereby promoting local tourism. Furthermore, tourists often seek out a little-known good place, but what constitutes a secret spot often needs to be clarified. This research defines “personalized secret spots” as places particularly special to an individual and investigates ways to identify them. In conclusion, different age groups prefer different tourist routes, and we succeed in detecting their unique spots.