Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums

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.

Quality of Experience in Cyber-Physical Social Systems Based on Reinforcement Learning and Game Theory

“…However, the visiting time is longer when the visitor pays for the visit. Additionally, in References [13,14], the authors considered a cost optimization formulation to solve the planning problem and to determine the optimal tour for each visitor given a set of visitor preferences and constraints.…”

Virtual Assistants for the Cultural Heritage Domain

“…Several applications were developed that deliver a personalized museum guide. Mathias et al [2] proposed a new method for personalized museum tour recommendations. Their research tackles the problem of optimizing museum visits according to visitor's preferences and artwork importance.…”

“…We developed a cloud-based platform Asistent [2,7] for the creation of virtual assistants and during a national project we created and integrated several VAs for web pages of municipalities in Slovenia. The developed web platform supports the creation, management and integration of virtual assistants to an arbitrary web page.…”

An efficient encoding scheme for a new multiple-type museum visitor routing problem with must-see and select-see exhibition rooms

“…Constraints (2) and (3) restrict that each group can visit one exhibition room at a time. Constraints (4) and (5) confine that each exhibition room can only contain one group at a time.…”

“…For individual visitors, Le Berre et al 1 studied a personalized museum routing problem. Based upon the visitor's preference of exhibits and other requirements, e.g., time restriction, they developed a planning approach to design a personalized schedule such that his/her satisfaction was Based upon various interest functions of artwork objective values, Mathias et al 2 proposed an integer linear programming and a natural language processing to the schedule optimal route for individual visitors. Lykourentzou et al 3 classified individual visitors into four animal metaphors, namely, ant, butterfly, fish and grasshopper, and also pointed that smart routing and recommendations can improve the quality of experience of museum for visitors.…”