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 oo/ooen 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. OEe evaluation shows the e,ectiveness of the proposed approach in predicting paths in terms of model perplexity on the test set.
Predicting your next stop-over from location-based social network data with recurrent neural networks
RECSYS 2017, 2nd ACM International Workshop on Recommenders in Tourism (RecTour'17), CEUR Proceedings Vol. 1906, August 27-31, 2017, Como, Italy
© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in RECSYS 2017, 2nd ACM International Workshop on Recommenders in Tourism (RecTour'17), CEUR Proceedings Vol. 1906, August 27-31, 2017, Como, Italy
PERMALINK : https://www.eurecom.fr/publication/5301