Activity recommendation systems aims at providing relevant information depending on targeted users' groups. For instance in a city, it makes sense to differentiate local residents from tourists. This research investigates to what extent the anonymized data collected from social networks can be used as a basis for making activity recommendations associated with local residents versus tourists when visiting a public place, such as a museum or gallery. Using rules based on the spatial, temporal and semantics of visited places, we are able to infer if a user is likely to be local or a tourist, based on anonymous sample Foursquare data and place-based semantics retrieved using Google Places API. Using semantics of visited places, it becomes possible to infer additional information about a user based on their movements over space and time. Depending on the kind and frequency of visited places, inferences about the aim of a visit to a location are possible. This analysis could provide information to users in the form of recommendations based on their movements while travelling around an area. This study has been performed using Foursquare check-ins for visitors to the Art Institute of Chicago between March 2010 and January 2011.
['Pierre Hallot', 'Kathleen Stewart', 'Roland Billen']
Who are my visitors and where do they come from?: an analysis based on foursquare check-ins and place-based semantics