Unlike expertise location systems which users query actively when looking for an expert, expert recommender systems suggest individuals without the context of a specific problem. An interesting research question is whether expert recommender systems should consider a users' social context when recommending potential research collaborators. One may argue that it might be easier for scientists to collaborate with colleagues in their social network, because initiating collaboration with socially unconnected researchers is burdensome and fraught with risk, despite potentially relevant expertise. However, many scientists also initiate collaborations outside of their social network when they seek to work with individuals possessing relevant expertise or acknowledged experts. In this paper, we studied how well content-based, social and hybrid recommendation algorithms predicted co-author relationships among a random sample of 17,525 biomedical scientists. To generate recommendations, we used authors' research expertise inferred from publication metadata and their professional social networks derived from their coauthorship history. We used 80% of our data set (articles published before 2007) as our training set, and the remaining data as our test set (articles published in 2007 or later). Our results show that a hybrid algorithm combining expertise and social network information outperformed all other algorithms with regards to Top 10 and Top 20 recommendations. For the Top 2 and Top 5 recommendations, social network-based information alone generated the most useful recommendations. Our study provides evidence that integrating social network information in expert recommendations may outperform a purely expertise-based approach.
- Biomedical research
- Expert recommendation
- Medical subject headings (MeSH)
- Social networks
ASJC Scopus subject areas
- Information Systems
- Library and Information Sciences