IEEE Project Abstract

Recommender Systems are increasingly playing an important role in our life, enabling users to find what they needwithin large data collections and supporting a variety of applications,from e-commerce to e-tourism. In this paper, we present a Big Data architecture supporting typical Cultural Heritage applications.On the top of querying, browsing and analysing cultural contents coming from distributed and heterogeneous repositories,we propose a novel user-centred recommendation strategy for cultural items suggestion. Despite centralising the processing operations within the cloud, the vision of edge intelligence has been exploited by having a mobile app (Smart Search Museum) to perform semantic searches and machine learning-based inference so as to be capable of suggesting museums, together with other items of interest, to users when they are visiting a city, exploiting jointly recommendation techniques and edge artificial intelligence facilities. Experimental results on accuracy and user satisfaction show the goodness of the proposed application.