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项目利用数字化支持来提高图书馆的吸引力,改善用户体验。该项目实现了一个应用程序,可帮助用户决策过程,提供经由推荐系统(RecSys)生成的书籍列表,以及通过交互式虚拟现实(VR)基于图形用户界面(GUI)展示它们。在本篇论文中,我们专注于设计和测试推荐系统,利用意大利都灵联网图书馆过去9年中所有用户借阅的数据。此外,我们利用Anobii在线读者社交网络收集的数据,他们分享了有关他们阅读的书籍的反馈和其他信息。在这些异构数据的支持下,我们建立并评估了基于内容(CB)和协同过滤(CF)的方法。我们的结果显示,CF优于CB方法,使读者的相关推荐提供性能提高了高达47%。然而,CB方法的性能非常依赖于读者已经阅读的书籍数量,并且对于有着大量历史记录的用户,其效果可以比CF更好。最后,我们的评估显示,如果该系统集成和利用Anobii数据集中的信息,则两种方法的性能显著提高,这使得我们可以包括更多用户的阅读数据(对于CF)和更丰富的书籍元数据(对于CB)。