Using Location for Personalized POI Recommendations in Mobile Environments
SAINT '06 Proceedings of the International Symposium on Applications on Internet
Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics"
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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In this demo we present Flarty, a mobile location-based social network for the dynamic construction and recommendation of art routes in the city of Florence, Italy, via item based similarity algorithms, places topic extraction and user interest modeling. To achieve this goal Flarty derives knowledge from users check-ins and combines clustering techniques and recommendation algorithms, as well as features such as geo-location, to define groups of similar artworks or POIs (Points Of Interest) and to compute the most efficient routes likely to meet user's interests. Model analysis takes into account ratings, topics extracted from textual features associated with the POIs, and users preferences computed exploiting collaborative filtering techniques on their past behavior.