P. Hallot, K. Stewart, and Roland Billen
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.
Categories and Subject Descriptors (according to ACM CCS): I.3.3
[Computer Graphics]: Modeling Methodologies, Visualization theory, concepts
and paradigms
full paper
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