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Recommending collaborators using social features and MeSH terms

Lee, DH and Brusilovsky, P and Schleyer, T (2011) Recommending collaborators using social features and MeSH terms. In: UNSPECIFIED UNSPECIFIED. ISBN UNSPECIFIED

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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 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.


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Item Type: Book Section
Status: Published
CreatorsEmailPitt UsernameORCID
Lee, DHhyl12@pitt.eduHYL12
Brusilovsky, Ppeterb@pitt.eduPETERB0000-0002-1902-1464
Schleyer, Ttitus@pitt.eduTITUS0000-0003-1829-971X
Date: 1 December 2011
Date Type: Publication
Journal or Publication Title: Proceedings of the ASIST Annual Meeting
Volume: 48
Event Type: Conference
DOI or Unique Handle: 10.1002/meet.2011.14504801025
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Date Deposited: 25 Apr 2012 19:41
Last Modified: 30 Oct 2017 03:55


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