Abstract Following the period marked by the spread of the COVID-19 virus, tourists and enthusiasts flocked to museums and archaeological parks, highlighting persistent and emerging challenges concerning the enhancement of cultural heritage. This study proposes a novel framework that leverages context-aware recommender systems (CARS) to personalize the cultural experience based on the contextual conditions in which it occurs. The framework has been specifically developed and tested within the archaeological sites of Paestum and Pompeii. The evaluation process was conducted in two phases. The first phase focused on assessing the accuracy and reliability of the proposed context-aware approach, using a dataset comprising 972 user ratings collected from visitors to the Pompeii archaeological park. The second phase measured user satisfaction through a large-scale, in situ evaluation conducted directly within the archaeological parks. This phase involved nearly 2000 participants and aimed to assess the real-world usability and perceived satisfaction of the platform through direct interaction in cultural settings. The results confirm the effectiveness of the approach, with over 90% of respondents expressing a high level of satisfaction.

In the Internet era, where information and communication technologies (ICT) allow data exchange, new tools able to select the correct data are needed. In this field, Recommender Systems have a prime location. The recommendation methods take many forms based on the information exploited in order to provide rating forecasts. However, all of them can choose the correct information to support users. Indeed, these methods allow for overcoming the information overload problem. This paper introduces the theoretical bases of a novel recommendation method defined Rating Singular Value Decomposition (RSVD). RSVD is a Content-Based method that exploits the Singular Value Decomposition properties in order to calculate rating forecasts. This method aims to elaborate the users and items profile to obtain matrices related to ones obtained in Collaborative Filtering methods that exploit Singular Value Decomposition. The accuracy of RSVD is compared with the accuracy of Collaborative Filtering methods, and a study on the sparsity problem is performed. The results obtained are promising.