Document - On learning prediction models for tourists paths

2015

Journal article Open Access

On learning prediction models for tourists paths

Muntean C. I., Nardini F. M., Silvestri F., Baraglia R.

Artificial Intelligence Theoretical Computer Science Learning to rank Geographical PoI prediction

In this article, we tackle the problem of predicting the "next" geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-theart in recommender and trail prediction systems for tourism, as well as a popularity baseline. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions.

Source: ACM transactions on intelligent systems and technology (Print) 7 (2015): 8–35. doi:10.1145/2766459

Publisher: Association for Computing Machinery, New York, NY , Stati Uniti d'America

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@article{oai:it.cnr:prodotti:345635,
	title = {On learning prediction models for tourists paths},
	author = {Muntean C. I. and Nardini F. M. and Silvestri F. and Baraglia R.},
	publisher = {Association for Computing Machinery, New York, NY  , Stati Uniti d'America},
	doi = {10.1145/2766459},
	journal = {ACM transactions on intelligent systems and technology (Print)},
	volume = {7},
	pages = {8–35},
	year = {2015}
}