One-class support vector machines for personalized tag-based resource classification in social bookmarking systems

Autores
Godoy, Daniela Lis
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Social tagging systems allow users to easily create, organize and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time-consuming. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class Support Vector Machine (SVM) classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is not an straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag-based classification are compared with those obtained using more traditional information sources such as the full-text of Web pages. Experimental evaluation showed that tag-based classifiers outperformed those learned using the text of documents as well as other content-related sources. Moreover, tag-based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g. tagging of photos or videos).
Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Materia
Social Tagging Systems
One-Class Classification
Social Media Search
Folksonomies
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/6843

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spelling One-class support vector machines for personalized tag-based resource classification in social bookmarking systemsGodoy, Daniela LisSocial Tagging SystemsOne-Class ClassificationSocial Media SearchFolksonomieshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Social tagging systems allow users to easily create, organize and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time-consuming. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class Support Vector Machine (SVM) classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is not an straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag-based classification are compared with those obtained using more traditional information sources such as the full-text of Web pages. Experimental evaluation showed that tag-based classifiers outperformed those learned using the text of documents as well as other content-related sources. Moreover, tag-based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g. tagging of photos or videos).Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaWiley2012-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/6843Godoy, Daniela Lis; One-class support vector machines for personalized tag-based resource classification in social bookmarking systems; Wiley; Concurrency and Computation: Practice & Experience; 24; 7; 1-2012; 2193-22061532-0626enginfo:eu-repo/semantics/altIdentifier/doi/10.1002/cpe.2892info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/cpe.2892/fullinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:43:39Zoai:ri.conicet.gov.ar:11336/6843instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:43:39.546CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
title One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
spellingShingle One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
Godoy, Daniela Lis
Social Tagging Systems
One-Class Classification
Social Media Search
Folksonomies
title_short One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
title_full One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
title_fullStr One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
title_full_unstemmed One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
title_sort One-class support vector machines for personalized tag-based resource classification in social bookmarking systems
dc.creator.none.fl_str_mv Godoy, Daniela Lis
author Godoy, Daniela Lis
author_facet Godoy, Daniela Lis
author_role author
dc.subject.none.fl_str_mv Social Tagging Systems
One-Class Classification
Social Media Search
Folksonomies
topic Social Tagging Systems
One-Class Classification
Social Media Search
Folksonomies
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Social tagging systems allow users to easily create, organize and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time-consuming. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class Support Vector Machine (SVM) classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is not an straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag-based classification are compared with those obtained using more traditional information sources such as the full-text of Web pages. Experimental evaluation showed that tag-based classifiers outperformed those learned using the text of documents as well as other content-related sources. Moreover, tag-based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g. tagging of photos or videos).
Fil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
description Social tagging systems allow users to easily create, organize and share collections of Web resources in a collaborative fashion. Videos, pictures, research papers and Web pages are shared and annotated in sites such as Del.icio.us, CiteULike or Flickr, among others. The rising popularity of these systems leads to a constant increase in the number of users actively publishing and annotating resources and, consequently, an exponential growth in the amount of data contained in their folksonomies, the underlying data structure of tagging systems. In turn, the user task of discovering interesting resources becomes more and more difficult and time-consuming. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class Support Vector Machine (SVM) classification is evaluated as a means to identify relevant information for users based exclusively on positive examples of their information preferences. It is assumed that users express their interest on resources belonging to a folksonomy by assigning tags to them, whereas there is not an straightforward method to collect uninterestingness judgments. Filtering interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities of Web communities. In this paper, the results achieved with tag-based classification are compared with those obtained using more traditional information sources such as the full-text of Web pages. Experimental evaluation showed that tag-based classifiers outperformed those learned using the text of documents as well as other content-related sources. Moreover, tag-based classification becomes essential for folksonomies in which no additional content is available because of the nature of resources being stored (e.g. tagging of photos or videos).
publishDate 2012
dc.date.none.fl_str_mv 2012-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/6843
Godoy, Daniela Lis; One-class support vector machines for personalized tag-based resource classification in social bookmarking systems; Wiley; Concurrency and Computation: Practice & Experience; 24; 7; 1-2012; 2193-2206
1532-0626
url http://hdl.handle.net/11336/6843
identifier_str_mv Godoy, Daniela Lis; One-class support vector machines for personalized tag-based resource classification in social bookmarking systems; Wiley; Concurrency and Computation: Practice & Experience; 24; 7; 1-2012; 2193-2206
1532-0626
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/cpe.2892
info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/cpe.2892/full
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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