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 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 / Palumbo, Enrico; Rizzo, Giuseppe; Troncy, Raphaël; Baralis, ELENA MARIA. - ELETTRONICO. - 1906:(2017). ((Intervento presentato al convegno RecTour@RecSys2017 tenutosi a Como nel 27/08/2017.

Predicting Your Next Stop-over from Location-based Social Network Data with Recurrent Neural Networks

PALUMBO, ENRICO;RIZZO, GIUSEPPE;BARALIS, ELENA MARIA
2017

Abstract

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 e‚ectiveness of the proposed approach in predicting paths in terms of model perplexity on the test set
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2678878
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