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Conference Paper: Towards a better similarity measure for keyword profiling via clustering

TitleTowards a better similarity measure for keyword profiling via clustering
Authors
Liang, YChow, KPHui, LCKFang, JYiu, SMHou, SH
KeywordsKeyword clustering
Similarity measure
User profiling
Issue Date2013
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000143
Citation
The IEEE 37th Annual Computer Software and Applications Conference Workshops (COMPSACW 2013), Kyoto, Japan, 22-26 July 2013. In IEEE International Computer Software and Applications Conference Proceedings, 2013, p. 16-20 How to Cite?
DOI: http://dx.doi.org/10.1109/COMPSACW.2013.19
AbstractAutomatic profiling for users and postings can help law enforcement units cluster and classify users and postings effectively so that potential problematic users and postings can be identified easily. A core problem in this application is to come up with effective profiles and a good measure to compare the similarity of two profiles. In this paper, we investigate an existing keyword-based user profiling scheme and identify its limitations. Then, we propose an improved version of it and demonstrate that our proposed version is more consistent than the existing approach with respect to the observed replied rates of a user to a posting based on the similarity of the profiles. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/189650
ISBN
978-0-7695-4987-3
ISSN
0730-3157
2020 SCImago Journal Rankings: 0.216
ISI Accession Number ID
WOS:000331223100004

 

DC FieldValueLanguage
dc.contributor.authorLiang, Yen_US
dc.contributor.authorChow, KPen_US
dc.contributor.authorHui, LCKen_US
dc.contributor.authorFang, Jen_US
dc.contributor.authorYiu, SMen_US
dc.contributor.authorHou, SHen_US
dc.date.accessioned2013-09-17T14:50:38Z-
dc.date.available2013-09-17T14:50:38Z-
dc.date.issued2013en_US
dc.identifier.citationThe IEEE 37th Annual Computer Software and Applications Conference Workshops (COMPSACW 2013), Kyoto, Japan, 22-26 July 2013. In IEEE International Computer Software and Applications Conference Proceedings, 2013, p. 16-20en_US
dc.identifier.isbn978-0-7695-4987-3-
dc.identifier.issn0730-3157-
dc.identifier.urihttp://hdl.handle.net/10722/189650-
dc.description.abstractAutomatic profiling for users and postings can help law enforcement units cluster and classify users and postings effectively so that potential problematic users and postings can be identified easily. A core problem in this application is to come up with effective profiles and a good measure to compare the similarity of two profiles. In this paper, we investigate an existing keyword-based user profiling scheme and identify its limitations. Then, we propose an improved version of it and demonstrate that our proposed version is more consistent than the existing approach with respect to the observed replied rates of a user to a posting based on the similarity of the profiles. © 2013 IEEE.-
dc.languageengen_US
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000143-
dc.relation.ispartofIEEE International Computer Software and Applications Conference Proceedingsen_US
dc.subjectKeyword clustering-
dc.subjectSimilarity measure-
dc.subjectUser profiling-
dc.titleTowards a better similarity measure for keyword profiling via clusteringen_US
dc.typeConference_Paperen_US
dc.identifier.emailChow, KP: chow@cs.hku.hken_US
dc.identifier.emailHui, LCK: hui@cs.hku.hken_US
dc.identifier.emailFang, J: jbfang@cs.hku.hken_US
dc.identifier.emailYiu, SM: smyiu@cs.hku.hken_US
dc.identifier.authorityChow, KP=rp00111en_US
dc.identifier.authorityHui, LCK=rp00120en_US
dc.identifier.authorityYiu, SM=rp00207en_US
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/COMPSACW.2013.19-
dc.identifier.scopuseid_2-s2.0-84885601972-
dc.identifier.hkuros223910en_US
dc.identifier.spage16en_US
dc.identifier.epage20en_US
dc.identifier.isiWOS:000331223100004-
dc.publisher.placeUnited Statesen_US
dc.customcontrol.immutablesml 131101-
dc.identifier.issnl0730-3157-

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