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Conference Paper: Who are experts specializing in landscape photography?: Analyzing topic-specific authority on content sharing services

TitleWho are experts specializing in landscape photography?: Analyzing topic-specific authority on content sharing services
Authors
KeywordsBayesian model
Content sharing services
Topic-specific authority analysis
Issue Date2014
PublisherACM.
Citation
The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, NY., 24-27 August 2014. In Conference Proceedings, 2014, p. 1506-1515 How to Cite?
AbstractWith the rapid growth of Web 2.0, a variety of content sharing services, such as Flickr, YouTube, Blogger, and TripAdvisor etc, have become extremely popular over the last decade. On these websites, users have created and shared with each other various kinds of resources, such as photos, video, and travel blogs. The sheer amount of user-generated content varies greatly in quality, which calls for a principled method to identify a set of authorities, who created high-quality resources, from a massive number of contributors of content. Since most previous studies only infer global authoritativeness of a user, there is no way to differentiate the authoritativeness in different aspects of life (topics). In this paper, we propose a novel model of Topic-specific Authority Analysis (TAA), which addresses the limitations of the previous approaches, to identify authorities specific to given query topic(s) on a content sharing service. This model jointly leverages the usage data collected from the sharing log and the favorite log. The parameters in TAA are learned from a constructed training dataset, for which a novel logistic likelihood function is specifically designed. To perform Bayesian inference for TAA with the new logistic likelihood, we extend typical Gibbs sampling by introducing auxiliary variables. Thorough experiments with two real-world datasets demonstrate the effectiveness of TAA in topic-specific authority identification as well as the generalizability of the TAA generative model. © 2014 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/203634
ISBN

 

DC FieldValueLanguage
dc.contributor.authorBi, Ben_US
dc.contributor.authorKao, Ben_US
dc.contributor.authorWan, Cen_US
dc.contributor.authorCho, Jen_US
dc.date.accessioned2014-09-19T15:49:08Z-
dc.date.available2014-09-19T15:49:08Z-
dc.date.issued2014en_US
dc.identifier.citationThe 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2014), New York, NY., 24-27 August 2014. In Conference Proceedings, 2014, p. 1506-1515en_US
dc.identifier.isbn978-1-4503-2956-9-
dc.identifier.urihttp://hdl.handle.net/10722/203634-
dc.description.abstractWith the rapid growth of Web 2.0, a variety of content sharing services, such as Flickr, YouTube, Blogger, and TripAdvisor etc, have become extremely popular over the last decade. On these websites, users have created and shared with each other various kinds of resources, such as photos, video, and travel blogs. The sheer amount of user-generated content varies greatly in quality, which calls for a principled method to identify a set of authorities, who created high-quality resources, from a massive number of contributors of content. Since most previous studies only infer global authoritativeness of a user, there is no way to differentiate the authoritativeness in different aspects of life (topics). In this paper, we propose a novel model of Topic-specific Authority Analysis (TAA), which addresses the limitations of the previous approaches, to identify authorities specific to given query topic(s) on a content sharing service. This model jointly leverages the usage data collected from the sharing log and the favorite log. The parameters in TAA are learned from a constructed training dataset, for which a novel logistic likelihood function is specifically designed. To perform Bayesian inference for TAA with the new logistic likelihood, we extend typical Gibbs sampling by introducing auxiliary variables. Thorough experiments with two real-world datasets demonstrate the effectiveness of TAA in topic-specific authority identification as well as the generalizability of the TAA generative model. © 2014 ACM.-
dc.languageengen_US
dc.publisherACM.-
dc.relation.ispartofProceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Miningen_US
dc.subjectBayesian model-
dc.subjectContent sharing services-
dc.subjectTopic-specific authority analysis-
dc.titleWho are experts specializing in landscape photography?: Analyzing topic-specific authority on content sharing servicesen_US
dc.typeConference_Paperen_US
dc.identifier.emailKao, B: kao@cs.hku.hken_US
dc.identifier.emailWan, C: cwan@cs.hku.hk-
dc.identifier.authorityKao, B=rp00123en_US
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1145/2623330.2623752-
dc.identifier.scopuseid_2-s2.0-84907029440-
dc.identifier.hkuros237616en_US
dc.identifier.spage1506-
dc.identifier.epage1515-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 141010-

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