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ABSTRACT
This article describes a method to provide adapted visit tours in art museums according to the preferences expressed by the visitor and exhibits prestige. It is based on a dual approach with, on the one hand an automatic textual analysis of the official information available online (labels of exhibits) that allows to rank the exhibit attractiveness for a standard museum visitor. On the other hand, individual preferences are also taken into account to adapt the visit according to the personal cultural awareness of the visitor. We use operations research to solve a routing optimization problem, aiming at finding a visit tour with time constraints and maximization of the visitor satisfaction. Depending on the instance size and the problem scale, an integer linear programming (ILP) model and a greedy algorithm are proposed to recommend personalized visit tours and applied on two museums: ‘Musée de l’Orangerie’ in Paris and ‘National Gallery’ in London. The obtained results show that it is possible to recommend a good tour to visitors of an art museum by taking into account the common prestige of the exhibits and the individual interests, joining automatic text summarization and routing optimization in a limited geographical space.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
4. Note that for the ‘National Gallery’, we used the list ‘100 highlighted paintings’ available of the website of the museum.
5. Note that functions and
return the set of incoming and outgoing arcs of a vertex v, respectively.
6. We do not take into account the textual ranking process as it is only done once per museum before running the tour generation algorithm.