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How to measure the information similarity in unilateral relations: The case study of Delicious

Lee, D (2010) How to measure the information similarity in unilateral relations: The case study of Delicious. In: UNSPECIFIED UNSPECIFIED. ISBN 9781450302296

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In this paper, I describe a better way to compute the information similarity between two users who are unilaterally connected. Unilateral relations are unidirectional connections and gain attention with the success of social tagging and microblogging systems. The relations are convenient and less bounded since people can make the connection without mutual agreement once they perceive that other users' information is worth. Using a social bookmarking data set, Delicious, I found that the traditional item unit-based similarity measures are not enough to show the common interests between a pair of unilaterally connected users. The similarity measure on the higher level such as metadata (root address of each URL) and macro-level tags (tags regardless of the annotated information item) showed better results. The significantly better results in metadata and macro-tag level similarity were also shown in the indirect relations, as well. I interpreted this result to mean that semantic information such as metadata and tags represent users' cognitive understanding of corresponding information. Therefore, in social tagging systems, it is better to match users not based on item-level similarity but based on the similarity on a higher level which embeds more semantic meanings. © 2010 ACM.


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Item Type: Book Section
Status: Published
CreatorsEmailPitt UsernameORCID
Lee, Dhyl12@pitt.eduHYL12
Date: 1 September 2010
Date Type: Publication
Journal or Publication Title: Proceedings of the International Workshop on Modeling Social Media, MSM '10
Event Type: Conference
DOI or Unique Handle: 10.1145/1835980.1835981
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISBN: 9781450302296
Date Deposited: 24 Apr 2012 15:29
Last Modified: 29 Jan 2019 15:55


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