Patterns of information – clustering books and readers in open access libraries

Abstract

Open access libraries operate in a continuum between two distinct organisation models: online retailers versus ‘traditional’ libraries. Online retailers such as Amazon.com are successful in recom-mending additional items that match the specific needs of their customers. The success rate of the recommendation depends on knowledge of the individual customer: more knowledge about persons leads to better suggestions. Thus, to optimally profit from the retailers’ offerings, the client must be prepared to share personal information, leading to the question of privacy.In contrast, protection of privacy is a core value for libraries. The question is how open access librar-ies can offer comparable services while retaining the readers’ privacy. A possible solution can be found in analysing the preferences of groups of like-minded people: communities. According to Lynch (2002), digital libraries are bad at identifying or predicting the communities that will use their collections. It is however our intention to explore the possibility to uncover sets of documents with a meaningful connection for groups of readers – the communities. The solution depends on examining patterns of usage, instead of storing information about individual readers. This paper will investigate the possibility to uncover the preferences of user groups within an open access digital library using social networking analysis techniques.

Big in Japan, Zimbabwe or Brazil – global reach and national preferences for open access books

“…8 Furthermore, clusters of German, Dutch and Italian language books tend to be more downloaded by residents from the 'corresponding' countries. 9 A recent article investigating the effects of open access at the Springer Nature e-books platform also analysed regional effects. The authors concluded that when the publication's title contained either 'Africa' or 'Latin America', this slightly increased the readership from those regions.…”

Words Algorithm Collection - finding closely related open access books using text mining techniques

“…Several other experiments have been conducted on the OAPEN Library collection: creating groups of books based on usage data (Snijder, 2019) or categorising titles based on Wikipedia pages (Snijder, 2021). In the first experiment, the download patterns are analysed to find which books are regularly selected together.…”