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Social tags for resource discovery : a comparison between machine learning and user-centric approaches

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Social tags for resource discovery : a comparison between machine learning and user-centric approaches

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dc.contributor.author Khasfariyati Razikin
dc.contributor.author Goh, Dion Hoe-Lian
dc.contributor.author Chua, Alton Yeow Kuan
dc.contributor.author Lee, Chei Sian
dc.date.accessioned 2012-08-15T00:57:48Z
dc.date.available 2012-08-15T00:57:48Z
dc.date.copyright 2011
dc.date.issued 2012-08-15
dc.identifier.citation Khasfariyati, R., Goh, D. H. L., Chua, A. Y. K., & Lee, C. S. (2011). Social tags for resource discovery: a comparison between machine learning and user-centric approaches. Journal of Information Science, 37(4), 391–404.
dc.identifier.uri http://hdl.handle.net/10220/8389
dc.description.abstract The objective of this paper is to investigate the effectiveness of tags in facilitating resource discovery through machine learning and user-centric approaches. Drawing our dataset from a popular social tagging system, Delicious, we conducted six text categorization experiments using the top 100 frequently occurring tags. We also conducted a human evaluation experiment to manually evaluate the relevance of some 2000 documents related to these tags. The results from the text categorization experiments suggest that not all tags are useful for content discovery regardless of the tag weighting schemes. Moreover, there were cases where the evaluators did not perform as well as the classifiers, especially when there was a lack of cues in the documents for them to ascertain the relationship with the tag assigned. This paper discusses three implications arising from the findings and suggests a number of directions for further research.
dc.language.iso en
dc.relation.ispartofseries Journal of information science
dc.rights © 2011 The Author(s).
dc.subject DRNTU::Library and information science
dc.title Social tags for resource discovery : a comparison between machine learning and user-centric approaches
dc.type Journal Article
dc.contributor.school Wee Kim Wee School of Communication and Information
dc.identifier.doi http://dx.doi.org/10.1177/0165551511408847
dc.description.version Accepted version

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