D-Net

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    <identifier>oai:pumaoai.isti.cnr.it:cnr.isti/cnr.isti/2014-B6-001</identifier>

    <datestamp>2015-02-04</datestamp>

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      <dc:title>LearNext: learning to predict tourists movements</dc:title>

      <dc:creator>Baraglia, Ranieri</dc:creator>

      <dc:creator>Muntean, Cristina Ioana</dc:creator>

      <dc:creator>Nardini, Franco Maria</dc:creator>

      <dc:creator>Silvestri, Fabrizio</dc:creator>

      <dc:subject>Learning to rank</dc:subject>

      <dc:subject>Geographical PoI Prediction</dc:subject>

      <dc:subject>info:eu-repo/classification/acm/H.3.3 Information Storage and Retrieval</dc:subject>

      <dc:subject>info:eu-repo/classification/msc/Information systems</dc:subject>

      <dc:description>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.</dc:description>

      <dc:publisher>CEUR</dc:publisher>

      <dc:contributor>Roberto Basili, Fabio Crestani, Marco Pennacchiotti</dc:contributor>

      <dc:date>2014</dc:date>

      <dc:type>info:eu-repo/semantics/conferenceObject</dc:type>

      <dc:identifier>http://puma.isti.cnr.it/dfdownloadnew.php?ident=cnr.isti/cnr.isti/2014-B6-001</dc:identifier>

      <dc:language>en</dc:language>

      <dc:source>In: IIR 2014 - 5th Italian Information Retrieval Workshop (University of Roma Tor Vergata, 21-22 January 2014). Abstract, pp. 75 - 79. Roberto Basili, Fabio Crestani, Marco Pennacchiotti (eds.). (CEUR Workshop Proceedings, vol. 1127). CEUR, 2014.</dc:source>

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