An Expertise Recommender System Based on Data from an Institutional Repository (DiVA)

Authors : Milena Angelova, Vishnu Devagiri, Veselka Boeva, Peter Linde, Niklas Lavesson

Finding experts in academics is an important practical problem, e.g. recruiting reviewers for reviewing conference, journal or project submissions, partner matching for research proposals, finding relevant M. Sc. or Ph. D. supervisors etc.

In this work, we discuss an expertise recommender system that is built on data extracted from the Blekinge Institute of Technology (BTH) instance of the institutional repository system DiVA. The developed prototype system is evaluated and validated on information extracted from the BTH DiVA installation, concerning thesis supervision of researchers affiliated with BTH.

The extracted DiVA classification terms are used to build an ontology that conceptualizes the thesis domain supported by the university. The supervisor profiles of the tutors affiliated with the BTH are constructed based on the extracted DiVA data. These profiles can further be used to identify and recommend relevant subject thesis supervisors.