Harnessing a generalised user behaviour model for next-POI recommendation
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Recommender Systems (RSs) are commonly used in web applications to support users in finding items of their interest. In this paper we propose a novel RS approach that supports human decision making by leveraging data acquired in the physical world. We consider a scenario in which users' choices to visit points of interests (POIs) are tracked and used to generate recommendations for not yet visited POIs. We propose a novel approach to user behaviour mod-elling that is based on Inverse Reinforcement Learning (IRL). Two recommendation strategies based on the proposed behaviour model are also proposed; they generate recommendations that differ from the common approach based on user next action prediction. Our experimental analysis shows that the proposed approach outperforms state of the art models in terms of the overall utility the user gains by following the provided recommendations and the novelty of the recommended items.