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).

翻译：Reading&Machine项目利用数字化的支持来增强图书馆的吸引力并改善用户体验。该项目实现了一个应用程序，通过推荐系统（RecSys）生成用户可能感兴趣的书籍列表，并借助基于交互式虚拟现实（VR）的图形用户界面（GUI）向用户展示这些列表。本文聚焦于推荐系统的设计与测试，使用了意大利都灵图书馆网络过去9年所有用户的借阅数据。此外，我们还利用了在线社交阅读社区Anobii收集的数据，该社区用户分享他们的反馈以及所读书籍的额外信息。基于这些异构数据，我们构建并评估了基于内容（CB）和协同过滤（CF）的方法。结果表明，CF方法优于CB方法，为用户提供的相关推荐最多可提升47%。然而，CB方法的性能在很大程度上取决于用户已阅读的书籍数量，对于阅读历史丰富的用户，其表现甚至可能优于CF。最后，我们的评估强调，如果系统整合并利用Anobii数据集中的信息——该数据集允许我们纳入更多用户阅读记录（针对CF）和更丰富的书籍元数据（针对CB）——两种方法的性能都将显著提升。