Integrating Indoor Positioning, Recommendation, and Personalization to Enhance Museum Visitor Experiences

Personalization in Cultural Heritage (CH) settings is crucial for transforming visitor experiences into meaningful interactions accommodating diverse expectations and preferences. This research presents a holistic framework to enhance visitor experiences in CH physical institutions, like Galleries, Libraries, Archives, and Museums, through the combination of Indoor Positioning Systems (IPS), Recommender Systems, and Large Language Models (LLMs). Our Bluetooth beacon-based IPS implementation has been successfully deployed in a major gallery in Rome. The system covers 17 rooms and over 100 artworks and provides the user's position with high accuracy. We conceptualized a recommendation algorithm to optimize visitor engagement by progressively increasing mean dwell time while considering spatial and temporal constraints. Moreover, our experiments with LLM-generated audioguides demonstrate that visitors prefer content tailored to established visitor categories, validating our approach to personalization. These findings provide empirical support for personalized digital interpretation in GLAM contexts, though challenges remain regarding IPS precision over time and LLM hallucination mitigation. Future work will focus on collecting visitor interaction data, implementing the recommender system, and potentially releasing datasets to address the scarcity of CH-specific positioning and recommendation data.