Recommender systems are increasingly playing an important role in our life, enabling users to find 'what they need' within 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 analyzing cultural contents coming from distributed and heterogeneous repositories, we propose a novel user-centered recommendation strategy for cultural items suggestion. Despite centralizing 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.
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