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Conference Paper: On incentive-based tagging

TitleOn incentive-based tagging
Authors
KeywordsAllocation strategy
Optimal algorithm
Social tagging systems
Tag-based
Issue Date2013
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178
Citation
The 29th International Conference on Data Engineering (ICDE 2013), Brisbane, Australia, 8-11 April 2013. In International Conference on Data Engineering Proceedings, 2013, p. 685-696 How to Cite?
AbstractA social tagging system, such as del.icio.us and Flickr, allows users to annotate resources (e.g., web pages and photos) with text descriptions called tags. Tags have proven to be invaluable information for searching, mining, and recommending resources. In practice, however, not all resources receive the same attention from users. As a result, while some highly-popular resources are over-tagged, most of the resources are under-tagged. Incomplete tagging on resources severely affects the effectiveness of all tag-based techniques and applications. We address an interesting question: if users are paid to tag specific resources, how can we allocate incentives to resources in a crowd-sourcing environment so as to maximize the tagging quality of resources? We address this question by observing that the tagging quality of a resource becomes stable after it has been tagged a sufficient number of times. We formalize the concepts of tagging quality (TQ) and tagging stability (TS) in measuring the quality of a resource's tag description. We propose a theoretically optimal algorithm given a fixed 'budget' (i.e., the amount of money paid for tagging resources). This solution decides the amount of rewards that should be invested on each resource in order to maximize tagging stability. We further propose a few simple, practical, and efficient incentive allocation strategies. On a dataset from del.icio.us, our best strategy provides resources with a close-to-optimal gain in tagging stability. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/189631
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYang, XSen_US
dc.contributor.authorCheng, Ren_US
dc.contributor.authorMo, Len_US
dc.contributor.authorKao, Ben_US
dc.contributor.authorCheung, DWLen_US
dc.date.accessioned2013-09-17T14:50:30Z-
dc.date.available2013-09-17T14:50:30Z-
dc.date.issued2013en_US
dc.identifier.citationThe 29th International Conference on Data Engineering (ICDE 2013), Brisbane, Australia, 8-11 April 2013. In International Conference on Data Engineering Proceedings, 2013, p. 685-696en_US
dc.identifier.isbn978-1-4673-4910-9-
dc.identifier.issn1084-4627-
dc.identifier.urihttp://hdl.handle.net/10722/189631-
dc.description.abstractA social tagging system, such as del.icio.us and Flickr, allows users to annotate resources (e.g., web pages and photos) with text descriptions called tags. Tags have proven to be invaluable information for searching, mining, and recommending resources. In practice, however, not all resources receive the same attention from users. As a result, while some highly-popular resources are over-tagged, most of the resources are under-tagged. Incomplete tagging on resources severely affects the effectiveness of all tag-based techniques and applications. We address an interesting question: if users are paid to tag specific resources, how can we allocate incentives to resources in a crowd-sourcing environment so as to maximize the tagging quality of resources? We address this question by observing that the tagging quality of a resource becomes stable after it has been tagged a sufficient number of times. We formalize the concepts of tagging quality (TQ) and tagging stability (TS) in measuring the quality of a resource's tag description. We propose a theoretically optimal algorithm given a fixed 'budget' (i.e., the amount of money paid for tagging resources). This solution decides the amount of rewards that should be invested on each resource in order to maximize tagging stability. We further propose a few simple, practical, and efficient incentive allocation strategies. On a dataset from del.icio.us, our best strategy provides resources with a close-to-optimal gain in tagging stability. © 2013 IEEE.-
dc.languageengen_US
dc.publisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178-
dc.relation.ispartofInternational Conference on Data Engineering Proceedingsen_US
dc.rightsInternational Conference on Data Engineering. Proceedings. Copyright © IEEE, Computer Society.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAllocation strategy-
dc.subjectOptimal algorithm-
dc.subjectSocial tagging systems-
dc.subjectTag-based-
dc.titleOn incentive-based taggingen_US
dc.typeConference_Paperen_US
dc.identifier.emailCheng, R: ckcheng@cs.hku.hken_US
dc.identifier.emailKao, B: kao@cs.hku.hken_US
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_US
dc.identifier.authorityCheng, R=rp00074en_US
dc.identifier.authorityKao, B=rp00123en_US
dc.identifier.authorityCheung, DWL=rp00101en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDE.2013.6544866-
dc.identifier.scopuseid_2-s2.0-84881321244-
dc.identifier.hkuros222844en_US
dc.identifier.spage685-
dc.identifier.epage696-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 131023-

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