  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.
