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

2020

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

Moghtasedi S., Muntean C. I., 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.

Source: ASONAM 2020 - The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 348–352, Online conference, 7-10/12/2020

Publisher: ACM, Association for computing machinery, New York, USA

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@inproceedings{oai:it.cnr:prodotti:445283,
	title = {High-quality prediction of tourist movements using temporal trajectories in graphs},
	author = {Moghtasedi S. and Muntean C. I. and Nardini F. M. and Grossi R. and Marino A.},
	publisher = {ACM, Association for computing machinery, New York, USA},
	doi = {10.1109/asonam49781.2020.9381450},
	booktitle = {ASONAM 2020 - The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 348–352, Online conference, 7-10/12/2020},
	year = {2020}
}