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.
@article{arxiv.1501.01252,
title = {Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums},
author = {Mayeul Mathias and Assema Moussa and Fen Zhou and Juan-Manuel Torres-Moreno and Marie-Sylvie Poli and Didier Josselin and Marc El-Bèze and Andréa Carneiro Linhares and Francoise Rigat},
journal= {arXiv preprint arXiv:1501.01252},
year = {2015}
}
Comments
8 pages, 4 figures; Proceedings of the 2014 Federated Conference on Computer Science and Information Systems pp. 439-446