Work

Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums

Year: 2014

Type: conference-paper

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.

Source: Annals of Computer Science and Information Systems

DOI: 10.15439/2014f336

Authors: Mayeul Mathias, Assema Moussa, Juan-Manuel Torres-Moreno, Fen Zhou, Marie-Sylvie Poli, Didier Josselin, Marc El-Bèze, Andréa Carneiro Linhares, Françoise Rigat

Institutions: Université d'Avignon et des Pays de Vaucluse, Laboratoire Informatique d'Avignon, University of Turin, Centre National de la Recherche Scientifique, Sciences et Technologies des Cultures et Sociétés Numériques

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2075173141

FWCI: 0.852

Cites: 7

Cited by: 8

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