How random walks can help tourism (Contributo in atti di convegno)

Type
Label
  • How random walks can help tourism (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-28997-2_17 (literal)
Alternative label
  • Lucchese C., Perego R., Silvestri F., Vahabi H., Venturini R. (2012)
    How random walks can help tourism
    in Advances in Information Retrieval. 34th European Conference on IR Research, Barcelona, Spain, 1-5 April 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Lucchese C., Perego R., Silvestri F., Vahabi H., Venturini R. (literal)
Pagina inizio
  • 195 (literal)
Pagina fine
  • 206 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Progetto VIsual Support to Interactive TOurism in Tuscany - Acronimo VISITO Tuscany - Tipo Progetto EU_FP7 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/2080751703368528/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Lecture Notes in Computer Science (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 7224 (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; IMT, Lucca & ISTI-CNR, Pisa, Italy; CNR-ISTI, Pisa, Italy; (literal)
Titolo
  • How random walks can help tourism (literal)
Abstract
  • On-line photo sharing services allow users to share their touristic experiences. Tourists can publish photos of interesting locations or monuments visited, and they can also share comments, annotations, and even the GPS traces of their visits. By analyzing such data, it is possible to turn colorful photos into metadata-rich trajectories through the points of interest present in a city. In this paper we propose a novel algorithm for the interactive gen- eration of personalized recommendations of touristic places of interest based on the knowledge mined from photo albums and Wikipedia. The distinguishing features of our approach are multiple. First, the underlying recommendation model is built fully automatically in an unsupervised way and it can be easily extended with heterogeneous sources of infor- mation. Moreover, recommendations are personalized according to the places previously visited by the user. Finally, such personalized recom- mendations can be generated very efficiently even on-line from a mobile device. (literal)
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