Personalized tourism route recommendation based on user's active interests

Tourism is both an important industry and popular leisure activity undertaken by millions around the world. How to effectively mine the user's travel mode and visit preferences based on the user's historical travel data is a challenge. The tourism resources of different visiting areas, such as the popularity of POI (Point of Interest), influence the user's interests dynamically. Therefore, a user's interest preferences during traveling may differ between geographical region. In this paper, we introduce a personalized travel route recommendation framework, named PTDR, based on region dependent personal interest. PTDR consists of two parts, which are POI recommendation and itinerary generation. We analyzed the user's history interest from check-in behavior in detail and constructed a convolutional neural network to extract the potential features of the target visiting area. Then the user's active interest is learned from the user's history interest and the potential features of the target area. Finally, we optimize the itinerary for a user based on the orienteering problem, which takes into account the user's travel restrictions, such as time limits, starting attraction restrictions, and destination attraction restrictions. We evaluated the proposed algorithm on four cities of Flickr datasets and compared them to existing travel recommendations, including accuracy, recall, and F1. Experiments verify the effectiveness of the proposed method.