LearNext: learning to predict tourists movements

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

Learning to rank  Geographical PoI Prediction  Information Storage and Retrieval 

In this paper, 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 Rank- ing 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-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.

Source: 5th Italian Information Retrieval Workshop, pp. 75–79, University of Roma Tor Vergata, 21-22 January 2014