High-quality prediction of tourist movements using temporal trajectories in graphs

Moghtasedi S., Muntean C., Nardini F. M., Grossi R., Marino A.

PoI prediction  Graph  Similarity  Temporal trajectory 

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.

Publisher: ACM, Association for computing machinery

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