When users in a digital library read or browse online resources, it generates an immense amount of data. If the underlying system can recommend items, such as books and journals, to the users, it will help them to find the related items. This research analyzes a digital library's usage data to recommend items to its users, and it uses different clustering algorithms to design the recommender system. We have used content-based clustering, including hierarchical, expectation maximization (EM), K-mean, FarthestFirst, and density-based clustering algorithms, and user access pattern-based clustering, which uses a hypergraph-based approach to generate the clusters. This research shows that the recommender system designed using the hypergraph algorithm generates the most accurate recommendation model compared to those designed using the content-based clustering approaches.

翻译：当数字图书馆用户阅读或浏览在线资源时，会产生海量数据。若底层系统能够向用户推荐图书、期刊等资源，将有助于用户发现相关物品。本研究通过分析数字图书馆的使用数据为用户推荐资源，并采用不同聚类算法设计推荐系统。我们使用了基于内容的聚类方法（包括层次聚类、期望最大化（EM）聚类、K均值聚类、最远优先聚类和密度聚类）以及基于用户访问模式的聚类方法（采用超图方法生成聚类）。研究表明，与基于内容的聚类方法相比，采用超图算法设计的推荐系统能够生成最精确的推荐模型。