A Multilevel Graph-Based Recommender System for Personalized Learning Paths in Archaeological Parks: Leveraging IoT and Situation Awareness

Enhancing Cultural Heritage relies on innovative technologies to improve user interaction with cultural assets. The advent of the Internet of Things (IoT) has made integrating smart devices with educational methodologies possible, enabling a combination of cultural engagement, heritage promotion, and learning. This study aims to introduce a Recommender System capable of suggesting personalized learning paths for users visiting archaeological parks, leveraging a multilevel graph-based approach. The method is grounded in Situation Awareness (SA) and structured into three main levels: perception, comprehension, and prediction. The perception level is ensured through data acquisition from sensors deployed in the field; the comprehension level utilizes semantic and contextual graph approaches for domain representation; and the prediction level is developed using predictive algorithms based on Bayesian Networks. A preliminary experimental campaign conducted across three archaeological parks allowed for testing the effectiveness of the proposed approach, demonstrating its predictive capabilities and potential in creating tailored cultural experiences. The findings highlight how advanced technologies can enrich users’ educational experiences and significantly contribute to the valorization of cultural heritage.