Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums
This work addresses personalized tour recommendations for museum visitors, but it appears incremental as it builds on existing optimization and NLP techniques.
The paper tackles the problem of generating personalized museum tours by combining visitor preference optimization with NLP-based artwork importance extraction, showing that the model improves visitor satisfaction in numerical experiments.
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.