Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums

TL;DR

This paper introduces a novel NLP-based optimization method for personalized museum tour recommendations, enhancing visitor satisfaction by automatically extracting artwork importance and optimizing preferences.

Contribution

It presents a new approach combining NLP and optimization for personalized museum tours, integrating interdisciplinary research and demonstrating improved visitor satisfaction.

Findings

Model improves visitor satisfaction in experiments

Automatic extraction of artwork importance is effective

Potential for personalized museum visit applications

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.