R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Hierarchical Spatial Data Structures
SSD '89 Proceedings of the First Symposium on Design and Implementation of Large Spatial Databases
PILGRIM: A Location Broker and Mobility-Aware Recommendation System
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
An Online Recommender System for Large Web Sites
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Travel route recommendation using geotags in photo sharing sites
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning travel recommendations from user-generated GPS traces
ACM Transactions on Intelligent Systems and Technology (TIST)
How random walks can help tourism
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
RecTour: A Recommender System for Tourists
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
LearNext: learning to predict tourists movements
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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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.