A Tour Recommendation System Considering Implicit and Dynamic Information

Tourism has become one of the world’s largest service industries. Due to the rapid development of social media, more people like self-guided tours than package itineraries planned by travel agencies. Therefore, how to develop itinerary recommendation systems that can provide practical tour suggestions for tourists has become an important research topic. This study proposes a novel tour recommendation system that considers the implicit and dynamic information of Point-of-Interest (POI). Our approach is based on users’ photo information uploaded to social media in various tourist attractions. For each check-in record, we will find the POI closest to the user’s check-in Global Positioning System (GPS) location and consider the POI as the one they want to visit. Instead of using explicit information such as categories to represent POIs, this research uses the implicit feature extracted from the textual descriptions of POIs. Textual description for a POI contains rich and potential information describing the POI’s type, facilities, or activities, which makes it more suitable to represent a POI. In addition, this study considers visiting sequences when evaluating user similarity during clustering so that tourists in each sub-group hold higher behavior similarity. Next, the Non-negative Matrix Factorization (NMF) dynamically derives the staying time for different users, time slots, and POIs. Finally, a personalized itinerary algorithm is developed that considers user preference and dynamic staying time. The system will recommend the itinerary with the highest score and the longest remaining time. A set of experiments indicates that the proposed recommendation system outperforms state-of-the-art next POI recommendation methods regarding four commonly used evaluation metrics.