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Content Provider | ACM Digital Library |
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Author | Silvestri, Fabrizio Baraglia, Ranieri Nardini, Franco Maria Muntean, Cristina Ioana |
Abstract | 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 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-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. |
Starting Page | 751 |
Ending Page | 756 |
Page Count | 6 |
File Format | |
ISBN | 9781450322638 |
DOI | 10.1145/2505515.2505656 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2013-10-27 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Geographical poi prediction Learning to rank |
Content Type | Text |
Resource Type | Article |
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