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.

Integrating modern and innovative technologies such as the Internet of Things (IoT) and Machine Learning (ML) presents new opportunities in healthcare, especially in medical spa therapies. Once considered palliative, these therapies conducted using mineral/thermal water are now recognized as a targeted and specific therapeutic modality. The peculiarity of these treatments lies in their simplicity of administration, which allows for prolonged treatments, often lasting weeks, with progressive and controlled therapeutic effects. Thanks to new technologies, it will be possible to continuously monitor the patient, both on-site and remotely, increasing the effectiveness of the treatment. In this context, wearable devices, such as smartwatches, facilitate non-invasive monitoring of vital signs by collecting precise data on several key parameters, such as heart rate or blood oxygenation level, and providing a perspective of detailed treatment progress. The constant acquisition of data thanks to the IoT, combined with the advanced analytics of ML technologies, allows for data collection and precise analysis, allowing real-time monitoring and personalized treatment adaptation. This article introduces an IoT-based framework integrated with ML techniques to monitor spa treatments, providing tailored customer management and more effective results. A preliminary experimentation phase was designed and implemented to evaluate the system’s performance through evaluation questionnaires. Encouraging preliminary results have shown that the innovative approach can enhance and highlight the therapeutic value of spa therapies and their significant contribution to personalized healthcare.

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.