The Reading[&]Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve
the users’ experience. The project implements an application that helps the users in their decision-making process, providing
recommendation system (RecSys)-generated lists of books the users might be interested in, and showing them through an
interactive Virtual Reality (VR)-based Graphical User Interface (GUI). In this paper, we focus on the design and testing of the
recommendation system, employing data about all users’ loans over the past 9 years from the network of libraries located in
Turin, Italy. In addition, we use data collected by the Anobii online social community of readers, who share their feedback
and additional information about books they read. Armed with this heterogeneous data, we build and evaluate Content Based
(CB) and Collaborative Filtering (CF) approaches. Our results show that the CF outperforms the CB approach, improving
by up to 47% the relevant recommendations provided to a reader. However, the performance of the CB approach is heavily
dependent on the number of books the reader has already read, and it can work even better than CF for users with a large
history. Finally, our evaluations highlight that the performances of both approaches are significantly improved if the system
integrates and leverages the information from the Anobii dataset, which allows us to include more user readings (for CF) and
richer book metadata (for CB)