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How to measure information similarity in online social networks: A case study of Citeulike

Lee, Danielle H. and Brusilovsky, Peter (2017) How to measure information similarity in online social networks: A case study of Citeulike. Information Sciences, 418-41. pp. 46-60. ISSN 00200255

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In our current knowledge-driven society, many information systems encourage users to utilize their online social connections’ information collections actively as useful sources. The abundant information-sharing activities among online social connections could be valuable in enhancing and developing a sophisticated user information model. In order to leverage the shared information as a user information model, our preliminary job is to determine how to measure effectively the resulting patterns. However, this task is not easy, due to multiple aspects of information and the diversity of information preferences among social connections. Which similarity measure is the most representable for the common interests of multifaceted information among online social connections? This is the main question we will explore in this paper. In order to answer this question, we considered users’ self-defined online social connections, specifically in Citeulike, which were built around an object-centered sociality as the gold standard of shared interests among online social connections. Then, we computed the effectiveness of various similarity measures in their capabilities to estimate shared interests. The results demonstrate that, instead of focusing on monotonous bookmark-based similarities, it is significantly better to zero in on more cognitively expressible metadata-based similarities in accounting for shared interests.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Lee, Danielle H.suleehs@gmail.com0000-0003-2106-716X
Brusilovsky, Peterpeterb@pitt.edupeterb0000-0002-1902-1464
Date: 2017
Date Type: Publication
Journal or Publication Title: Information Sciences
Volume: 418-41
Page Range: pp. 46-60
DOI or Unique Handle: 10.1016/j.ins.2017.07.034
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
ISSN: 00200255
Official URL:
Article Type: Research Article
Date Deposited: 12 Dec 2018 19:01
Last Modified: 12 Dec 2018 19:01


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