http://www.cnr.it/ontology/cnr/individuo/prodotto/ID212277
A Trajectory-Based Recommender System for Tourism (Contributo in atti di convegno)
- Type
- Label
- A Trajectory-Based Recommender System for 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
- 10.1007/978-3-642-35236-2 (literal)
- Alternative label
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F. (2012)
A Trajectory-Based Recommender System for Tourism
in AMT 2012 - Active Media Technology 8th International Conference, Macau, China, December 4-7, 2012
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F. (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://link.springer.com/content/pdf/10.1007%2F978-3-642-35236-2_20.pdf (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Note
- PuMa (literal)
- Scopu (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- CNR-ISTI, Pisa, Italy;
University of Pisa, Italy;
Babes-Bolyai University, Cluj-Napoca, Romania;
CNR-ISTI, Pisa, Italy;
CNR-ISTI, Pisa, Italy; (literal)
- Titolo
- A Trajectory-Based Recommender System for Tourism (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-3-642-35236-2 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Runhe Huang, Ali A. Ghorbani, Gabriella Pasi, Takahira Yamaguchi, Neil Y. Yen, Beijing Jin (literal)
- Abstract
- Recommendation systems provide focused information to users on a set of objects belonging to a specific domain. The proposed recommender system provides personalized suggestions about touristic points of interest. The system generates recommendations, consisting of touristic places, according to the current position of a tourist and previously collected data describing tourist movements in a touristic location/city. The touristic sites correspond to a set of points of interest
identified a priori. We propose several metrics to evaluate both the spatial coverage of the dataset and the quality of recommendations produced. We assess our system on two datasets: a real and a synthetic one. Results show that our solution is a viable one. (literal)
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