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

Shima Moghtasedi, Cristina Ioana Muntean, Franco Maria Nardini, Roberto Grossi, Andrea Marino
2020 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)  
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 learningbased technique, as well as with a popularity baseline, using three public real-world datasets. Our
more » ... xperimental results show that our technique outperforms state-of-the-art machine learning-based methods effectively, by providing at least twice more accurate results.
doi:10.1109/asonam49781.2020.9381450 fatcat:xbfkilkffjdprnfl3esu5i7aqe