Next-POI Recommendations Matching User’s Visit Behaviour
David Massimo, Francesco Ricci⋆ · 2021
Abstract We consider the urban tourism scenario, which is characterized by limited availability of information about individuals’ past behaviour. Our system goal is to identify relevant next Points of Interest (POIs) recommendations. We propose a technique that addresses the domain requirements by using clusters of users’ visits trajectories that show similar visit behaviour. Previous analysis clustered visit trajectories by aggregating trajectories that contain similar POIs. We compare our approach with a next-item recommendation state-of-the-art Neighbour-based model. The results show that customizing recommendations for clusters of users’ with similar behaviour yields superior performance on different quality dimensions of the recommendation.