High-Quality Prediction of Tourist Movements using Temporal Trajectories in Graphs

Abstract

In this paper, we study the problem of predicting the next position of a tourist given his history. In particular, we propose a model to identify the next point of interest that a tourist will visit in the future, by making use of similarity between trajectories on a graph and taking into account the spatial-temporal aspect of trajectories. We compare our method with a well-known machine learning-based technique, as well as with a popularity baseline, using three public real-world datasets. Our experimental results show that our technique outperforms state-of-the-art machine learning-based methods effectively, by providing at least twice more accurate results.