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

By Enrico Palumbo, Giuseppe Rizzo, Raphaël Troncy and ELENA MARIA Baralis

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\u82ectiveness of the proposed approach in predicting paths in terms of model perplexity on the test se

Topics: Recurrent Neural Networks, Deep Learning, Next item recommendation
Publisher: CEUR
Year: 2017
OAI identifier: oai:iris.polito.it:11583/2678878

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