Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums
Mayeul Mathias, Assema Moussa, Juan-Manuel Torres-Moreno, Fen Zhou, Marie-Sylvie Poli, Didier Josselin, Marc El-Bèze, Andréa Carneiro Linhares, Françoise Rigat
Citation: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 2, pages 439–446 (2014)
Abstract. This paper proposes a new method to provide personalized tour recommendation for museum visits. It combines an optimization of preference criteria of visitors with an automatic extraction of artwork importance from museum information based on Natural Language Processing using textual energy. This project includes researchers from computer and social sciences. Some results are obtained with numerical experiments. They show that our model clearly improves the satisfaction of the visitor who follows the proposed tour. This work foreshadows some interesting outcomes and applications about on-demand personalized visit of museums in a very near future.