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Recommending collaborators using social features and MeSH terms by Danielle H. Lee; Peter Brusilovsky; Titus Schleyer is a Computer Science article available to read on EtoBox.

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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 20

Who reads Recommending collaborators using social features and MeSH terms?

Author

Danielle H. Lee; Peter Brusilovsky; Titus Schleyer

Publisher

Wiley (John Wiley & Sons); Wiley-Blackwell; Wiley (ISSN 0044-7870)

Published

2011

Language

EN

Field

Computer Science (Physical Sciences)