The rapid growth in the use of recommendation systems in the tourism sector is mainly related to the possibility to access updated data deriving from social networks, thus providing more appropriate and personalized suggestions. The paper presents a tourist trip recommendation system that suggests personalized itineraries defined as sequence of point of interest (PoI) to visit. The system core integrates two software modules: a neural network and an optimization engine. For every pair user-PoI typology, the neural network provides, on the basis of the analysis of the social media data, a score between 0 and 1. These latter values are then used as input parameters for a routing optimization problem that suggests the itinerary by considering additional restriction, as, for example, time windows, budget and time limitations, specified by the end user. Being a computational demanding problem, the model solution is carried out by applying a heuristic approach that is proven to provide high-quality solution in a limited amount of time.
Social media, neural network, routing problem
 Hendrik, H. & Perdana, D.H.F., Trip guidance: a linked data based mobile touristsguide. Advanced Science Letters, 20, pp. 75–79, 2014.
 Lee, C.-H., Kim, Y.-H. & Rhee, P.-K., Web personalization expert with combiningcollaborative filtering and association rule mining technique. Expert Systems andApplications, 21(3), pp. 131–137, 2001.
 Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., & Sartin, M.Combining Content-based and Collaborative Filters in an Online Newspaper. ACMSIGIR workshop on recommender systems. Berkeley, CA: ACM Press, 1999.
 Hasnat, M. & Hasan, S., Identifying tourists and analysing spatial patterns of theirdestinations from location-based social media data. Transportation Research Part C:Emerging Technologies, 96, pp. 38-54, 2018.
 Persia,F,, Pilato, G., Ge, M., Bolzoni, P., D’Auria, D. & Helmer, S., Improvingorienteering-based tourist trip planning with social sensing. Future GenerationComputer Systems, https://doi.org/10.1016/j.future.2019.10.028, 2019.
 Golden, B.L., Levy, L., & Vohra, R., The orienteering problem. Naval ResearchLogistics, 34(3), pp. 307–318, 1987.
 Gendreau, M., Laporte, G. & Semet, F., A tabu search heuristic for the undirectedselective travelling salesman problem. European Journal of Operational Research,106(2), pp. 539–545, 1998.
 Ciancio, C., De Maio, A., Laganà, D., Santoro, F. & Violi, A., A Genetic AlgorithmFramework for the Orienteering Problem with Time Windows. New Trends in EmergingComplex Real Life Problems, AIRO Springer Series, pp. 179–188, 2018.
 Beraldi, P., De Maio, A., Laganà, D. & Violi, A., A pick-up and delivery problem forlogistics e-marketplace services. Optimization Letters, https://doi.org/10.1007/s11590-019-01472-3, 2019.
 Bertazzi, L., Coelho, L.C., De Maio, A. & Laganà, D., A matheuristic algorithm for themulti-depot inventory routing problem. Transportation Research Part E: Logistics andTransportation Review, 122, pp. 524-544, 2019.
 Battarra, M., Erdoğan, G. & Vigo, D., Exact Algorithms for the Clustered VehicleRouting Problem, Operation Research, 62(1), pp. 58-71, 2014.
 Pop, P.C., Fuksz, L., Marc, A.H. & Sabo, C., A novel two-level optimization approachfor clustered vehicle routing problem. Computers & Industrial Engineering, 115, pp.304-318, 2018.