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.

Visitor studies have moved far beyond the simple gathering of statistics to develop increasingly refined data and behavioural profiles. The Centre for Study and Research on Exhibitions and Museums (Centre d'Études et de Recherche sur les Expositions et les Musées, CEREM) at Jean Monnet University, Saint‐Étienne, France, is a leader in the field and developed an innovative approach to shed new light on an old question: How do visitors perceive an exhibition?. Jean Davallon 1 is professor of sociology at the University and director of CEREM. Hanna Gottesdiener is professor of psychology at the University of Paris‐X, a member of CEREM and Editor‐in‐Chief of Publics et Musèes. Marie‐Sylvie Poli is lecturer in language sciences, Pierre Mendeèes France University, Grenoble, and a member of CEREM.

No abstract

plongée dans la matière… « Entrez dans le Nanomonde », premier module de l'exposition Nanotechnologies : infiniment petit, maxi défis © CCSTI Grenoble/Laurence Fragnol * Pascale Ancel est maître de conférences en Sociologie Marie-Sylvie Poli est professeur de Sciences du Langage au centre de Sociologie sur les représentations et les pratiques culturelles-Recherches sur les oeuvres et les mondes de l'Art, université Pierre Mendès France de Grenoble l a l e t t r e d e l ' o c i m n°118, juillet-août 2008 O p i n i o n p u b l i q u e e t n a n o t e c h n o l o g i e s