  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).
