2012
Conference article
Restricted
RecTour: a recommender system for tourists
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
This paper presents a recommender system that provides personalized information about locations of potential interest to a tourist. The system generates suggestions, consisting of touristic places, according to the current position and history data describing the tourist movements. For the selection of tourist sites, the system uses a set of points of interest a priori identified. We evaluate our system on two datasets: a real and a synthetic one, both storing trajectories describing previous movements of tourists. The proposed solution has high applicability and the results show that the solution is both efficient and viable.Source: International Workshop on Tourism Facilities, Macau, China, 4 December 2012
DOI: 10.1109/wi-iat.2012.88
Metrics:
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
This paper presents a recommender system that provides personalized information about locations of potential interest to a tourist. The system generates suggestions, consisting of touristic places, according to the current position and history data describing the tourist movements. For the selection of tourist sites, the system uses a set of points of interest a priori identified. We evaluate our system on two datasets: a real and a synthetic one, both storing trajectories describing previous movements of tourists. The proposed solution has high applicability and the results show that the solution is both efficient and viable.Source: International Workshop on Tourism Facilities, Macau, China, 4 December 2012
DOI: 10.1109/wi-iat.2012.88
Metrics:
See at:
doi.org 
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2012
Contribution to book
Restricted
Exploring the meaning behind Twitter hashtags through clustering.
Muntean C. I., Morar G. A., Moldovan D.
Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with.Source: BIS 2012 - Business Information Systems Workshops. Revised papers, edited by Witold Abramowicz, John Domingue, Krzysztof W?cel, pp. 231–242. London: Springer, 2012
DOI: 10.1007/978-3-642-34228-8_22
Metrics:
Muntean C. I., Morar G. A., Moldovan D.
Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with.Source: BIS 2012 - Business Information Systems Workshops. Revised papers, edited by Witold Abramowicz, John Domingue, Krzysztof W?cel, pp. 231–242. London: Springer, 2012
DOI: 10.1007/978-3-642-34228-8_22
Metrics:
See at:
doi.org | gateway.webofknowledge.com | link.springer.com | CNR ExploRA
2012
Conference article
Open Access
A trajectory-based recommender system for tourism.
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
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.Source: Active Media Technology. 8th International Conference, pp. 196–205, Macau, China, 4-7 December 2012
DOI: 10.1007/978-3-642-35236-2_20
Metrics:
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
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.Source: Active Media Technology. 8th International Conference, pp. 196–205, Macau, China, 4-7 December 2012
DOI: 10.1007/978-3-642-35236-2_20
Metrics:
See at:
hpc.isti.cnr.it 
| doi.org | link.springer.com | CNR ExploRA