In the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

In the world of Big Data, a tool capable of filtering data and providing choice support is crucial. Recommender Systems have this aim. These have evolved further through the use of information that would improve the ability to suggest. Among the possible exploited information, the context is widely used in literature and leads to the definition of the Context-Aware Recommender System. This paper proposes a Context-Aware Recommender System based on the concept of embedded context. This technique has been tested on different datasets to evaluate its accuracy. In particular, the use of multiple datasets allows a deep analysis of the advantages and disadvantages of the proposed approach. The numerical results obtained are promising.

After a period of closure, there has been a renaissance in the world of tourism, particularly cultural tourism, where it is necessary to guide the visitor to discover the arts and cultural heritage. This renewed interest has highlighted historical challenges and new issues, suggesting the need for innovative technologies to enhance the cultural experience. Technologies such as the Internet of Things (IoT) and Context and Situation Awareness make it possible to collect and analyze environmental data in real time, enabling dynamic adaptation of tourist routes to specific conditions. This study proposes a recommendation system based on semantic approaches and probabilistic graphs designed to offer hybrid paths, both real and virtual, aimed at enhancing the user experience based on visitor preferences and environmental conditions. Through a multimodal approach, the system enhances engagement through dynamic multimedia content, combining physical and virtual experiences. It allows users to overcome the physical limitations of specific points of interest while providing an immersive and personalized experience. The proposed approach was validated through two strategies. The first evaluates the accuracy of the suggestions made through the proposed approach to test its reliability. The second consists of an experimental campaign involving the implementation of cultural routes within the Archaeological Park of Pompeii, where satisfactory results were obtained.

The issue related to sustainability has become central in recent years. In this field, lithium-ion batteries have been widespread, especially in the automotive field, to reduce emissions. Their use, however, requires the ability to analyse the physical patterns related to the reactions that determine the charging and discharging phase of batteries in order to monitor them and verify the life cycle. For this purpose, it is necessary to identify solving methods that allow the accurate and efficient analysis of models related to lithium-ion batteries, which consist of partial differential equations of reaction-diffusion type. Thus, this work aims to analyse the behaviour of Physics-Informed Neural Networks (PINNs) in solving differential problems related to models describing battery charging and discharging, focusing on the latest developments in the literature. Therefore, in the experimental phase, test problems are analysed on which PINNs achieve an appropriate accuracy for a qualitative analysis of the models under consideration.