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Conference Paper: CubeLSI: an effective and efficient method for searching resources in social tagging systems

TitleCubeLSI: an effective and efficient method for searching resources in social tagging systems
Authors
KeywordsEfficient method
Feature space
IR techniques
Key word matching
Resource retrieval
Issue Date2011
PublisherIEEE, Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178
Citation
The IEEE 27th International Conference on Data Engineering (ICDE 2011), Hannover, Germany, 11-16 April 2011. In International Conference on Data Engineering Proceedings, 2011, p. 27-38 How to Cite?
AbstractIn a social tagging system, resources (such as photos, video and web pages) are associated with tags. These tags allow the resources to be effectively searched through tag-based keyword matching using traditional IR techniques. We note that in many such systems, tags of a resource are often assigned by a diverse audience of causal users (taggers). This leads to two issues that gravely affect the effectiveness of resource retrieval: (1) Noise: tags are picked from an uncontrolled vocabulary and are assigned by untrained taggers. The tags are thus noisy features in resource retrieval. (2) A multitude of aspects: different taggers focus on different aspects of a resource. Representing a resource using a flattened bag of tags ignores this important diversity of taggers. To improve the effectiveness of resource retrieval in social tagging systems, we propose CubeLSI a technique that extends traditional LSI to include taggers as another dimension of feature space of resources. We compare CubeLSI against a number of other tag-based retrieval models and show that CubeLSI significantly outperforms the other models in terms of retrieval accuracy. We also prove two interesting theorems that allow CubeLSI to be very efficiently computed despite the much enlarged feature space it employs. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/135695
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorBi, Ben_HK
dc.contributor.authorLee, SDen_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorCheng, Ren_HK
dc.date.accessioned2011-07-27T01:39:53Z-
dc.date.available2011-07-27T01:39:53Z-
dc.date.issued2011en_HK
dc.identifier.citationThe IEEE 27th International Conference on Data Engineering (ICDE 2011), Hannover, Germany, 11-16 April 2011. In International Conference on Data Engineering Proceedings, 2011, p. 27-38en_HK
dc.identifier.isbn978-1-4244-8960-2-
dc.identifier.issn1084-4627en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135695-
dc.description.abstractIn a social tagging system, resources (such as photos, video and web pages) are associated with tags. These tags allow the resources to be effectively searched through tag-based keyword matching using traditional IR techniques. We note that in many such systems, tags of a resource are often assigned by a diverse audience of causal users (taggers). This leads to two issues that gravely affect the effectiveness of resource retrieval: (1) Noise: tags are picked from an uncontrolled vocabulary and are assigned by untrained taggers. The tags are thus noisy features in resource retrieval. (2) A multitude of aspects: different taggers focus on different aspects of a resource. Representing a resource using a flattened bag of tags ignores this important diversity of taggers. To improve the effectiveness of resource retrieval in social tagging systems, we propose CubeLSI a technique that extends traditional LSI to include taggers as another dimension of feature space of resources. We compare CubeLSI against a number of other tag-based retrieval models and show that CubeLSI significantly outperforms the other models in terms of retrieval accuracy. We also prove two interesting theorems that allow CubeLSI to be very efficiently computed despite the much enlarged feature space it employs. © 2011 IEEE.en_HK
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_HK
dc.rightsInternational Conference on Data Engineering. Proceedings. Copyright © IEEE, Computer Society.-
dc.rights©2011 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.subjectEfficient method-
dc.subjectFeature space-
dc.subjectIR techniques-
dc.subjectKey word matching-
dc.subjectResource retrieval-
dc.titleCubeLSI: an effective and efficient method for searching resources in social tagging systemsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailBi, B: bbi@cs.hku.hken_HK
dc.identifier.emailLee, SD: sdlee@cs.hku.hken_HK
dc.identifier.emailKao, B: kao@cs.hku.hk-
dc.identifier.emailCheng, R: ckcheng@cs.hku.hk-
dc.identifier.authorityKao, B=rp00123en_HK
dc.identifier.authorityCheng, R=rp00074en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICDE.2011.5767863en_HK
dc.identifier.scopuseid_2-s2.0-79957801787en_HK
dc.identifier.hkuros186894en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79957801787&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage27en_HK
dc.identifier.epage38en_HK
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe IEEE 27th International Conference on Data Engineering (ICDE 2011), Hannover, Germany, 11-16 April 2011. In International Conference on Data Engineering Proceedings, 2011, p. 27-38-
dc.identifier.scopusauthoridCheng, R=7201955416en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridLee, SD=7601400741en_HK
dc.identifier.scopusauthoridBi, B=24558571800en_HK

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