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Next-POI Recommendations for the Smart Destination Era


A novel Recommender System exploiting behavioural users’ data in order to identify and recommend relevant and novel points of interest (POIs) is here presented. The proposed approach applies clustering to users’ sensed POI visit trajectories in order to identify like-behaving users and then it learns a distinct behaviour model for each cluster. The learnt behaviour model is used to generate novel and relevant recommendations for next POI visits that optimise the user reward, which is inferred from the data. In a live user study it is assessed, along different dimensions, how users evaluate recommendations produced by the proposed method in comparison with a traditional one. The results illustrate the differences between the compared approaches and the benefits of the proposed one.


  • Recommender systems
  • Smart tourism
  • Behaviour learning

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    However, still some recommendations can be not novel because the user will never declare all the POIS that she previously visited in the city.

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The research described in this paper was developed in the project Suggesto Market Space, which is funded by the Autonomous Province of Trento, in collaboration with Ectrl Solutions and Fondazione Bruno Kessler.

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Correspondence to David Massimo .

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Massimo, D., Ricci, F. (2020). Next-POI Recommendations for the Smart Destination Era. In: Neidhardt, J., Wörndl, W. (eds) Information and Communication Technologies in Tourism 2020. Springer, Cham.

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