{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T15:57:10Z","timestamp":1774195030887,"version":"3.50.1"},"reference-count":29,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2012,1,11]],"date-time":"2012-01-11T00:00:00Z","timestamp":1326240000000},"content-version":"vor","delay-in-days":375,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc of Assoc for Info"],"published-print":{"date-parts":[[2011,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010based, social and hybrid recommendation algorithms predicted co\u2010author 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 co\u2010authorship 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\u2010based information alone generated the most useful recommendations. Our study provides evidence that integrating social network information in expert recommendations may outperform a purely expertise\u2010based approach.<\/jats:p>","DOI":"10.1002\/meet.2011.14504801025","type":"journal-article","created":{"date-parts":[[2012,1,11]],"date-time":"2012-01-11T12:23:03Z","timestamp":1326284583000},"page":"1-10","source":"Crossref","is-referenced-by-count":10,"title":["Recommending collaborators using social features and MeSH terms"],"prefix":"10.1002","volume":"48","author":[{"given":"Danielle H.","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Brusilovsky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Titus","family":"Schleyer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2012,1,11]]},"reference":[{"key":"e_1_2_7_2_2","doi-asserted-by":"crossref","unstructured":"Ahn Y.\u2010Y. et al. Analysis of topological characteristics of huge online social networking services inProceedings of the 16th international conference on World Wide Web.2007 ACM: Banff Alberta Canada. p.835\u2013844.","DOI":"10.1145\/1242572.1242685"},{"key":"e_1_2_7_3_2","unstructured":"Bedrick S.andD.Sittig A scientific collaboration tool built on the facebook platform inProceedings of AMIA 2008 Annual Symposium.2008: Washington DC USA. p.41\u201345."},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72079-9_12"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.55.090902.142015"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/jamia.2001.0080317"},{"key":"e_1_2_7_7_2","unstructured":"Friedman P. et al. Development of a MeSH\u2010based index of faculty research interests. 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Lauderdale Florida USA. p.593\u2013600.","DOI":"10.1145\/642611.642714"},{"key":"e_1_2_7_16_2","doi-asserted-by":"crossref","unstructured":"McDonald D.W.andM.S.Ackerman Expertise recommender: a flexible recommendation system and architecture inProceedings of the 2000 ACM conference on Computer supported cooperative work.2000 ACM: Philadelphia Pennsylvania United States. p.231\u2013240.","DOI":"10.1145\/358916.358994"},{"key":"e_1_2_7_17_2","unstructured":"Medicine U.N.L.o.Fact Sheet: Medical Subject Headings (MeSH). May 24 2010]; Available from:http:\/\/www.nlm.nih.gov\/pubs\/factsheets\/mesh.html."},{"key":"e_1_2_7_18_2","unstructured":"Mohsen J.Modeling and Comparing the Influence of Neighbors on the Behavior of Users in Social and Similarity Networks.2010."},{"key":"e_1_2_7_19_2","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780199206650.001.0001"},{"key":"e_1_2_7_20_2","unstructured":"Opsahl T.Average shortest distance in weighted networks. 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