In the past years, Location-based Social Network (LBSN) data have
strongly fostered a data-driven approach to the recommendation
of Points of Interest (POIs) in the tourism domain. However, an
important aspect that is often not taken into account by current
approaches is the temporal correlations among POI categories in
tourist paths. In this work, we collect data from Foursquare, we
extract timed paths of POI categories from sequences of temporally
neighboring check-ins and we use a Recurrent Neural Network
(RNN) to learn to generate new paths by training it to predict
observed paths. As a further step, we cluster the data considering
users’ demographics and learn separate models for each category
of users. The evaluation shows the eectiveness of the proposed
approach in predicting paths in terms of model perplexity on the
test se
