Testing Collaborative Filtering against Co-Citation Analysis and Bibliographic Coupling for Academic Author Recommendation

Recommendation systems have become an important tool to overcome information overload and help people to make the right choice of needed items, which can be e.g. documents, products, tags or even other people. Last attribute has aroused our interest: Scientists are in need of different collaboration partners, i.e. experts for a special topic similar to their research field, to work with. Co-citation and bibliographic coupling have become standard measurements in scientometrics for detecting author similarity, but it can be laborious to elevate these data accurately. As collaborative filtering (CF) has proved to show acceptable results in recommender systems, we investigate in the comparison of scientometric analysis methods and CF methods. We use data from the social bookmarking service CiteULike as well as from the multi-discipline information services Web of Science and Scopus to recommend authors as potential collaborators for a target scientist. The paper aims to answer how a relevant author cluster for a target scientist can be proposed with CF and how the results differ in comparison with co-citation and bibliographic coupling. In this paper we will show first result, complemented by an explicit user evaluation with the help of the target authors.