期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 7, 期 1, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2766459
关键词
Algorithms; Design; Experimentation; Geographical PoI prediction; learning to rank
资金
- EU projects InGeoCLOUDS [297300]
- MIDAS [318786]
- E-CLOUD [325091]
- Italian PRIN 2011 project Algoritmica delle Reti Sociali Tecno-Mediate
- Regional (Tuscany) project SECURE! (FESR PorCreo)
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.
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