  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.
