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Modeling Social Annotation: A Bayesian Approach

Modeling Social Annotation: A Bayesian Approach Modeling Social Annotation: A Bayesian Approach ANON PLANGPRASOPCHOK National Electronics and Computer Technology Center and KRISTINA LERMAN USC Information Sciences Institute Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, for example, Web pages or scienti c papers, with descriptive labels called tags. The social annotations contributed by thousands of users can potentially be used to infer categorical knowledge, classify documents, or recommend new relevant information. Traditional text inference methods do not make the best use of social annotation, since they do not take into account variations in individual users ™ perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes the interests of individual annotators into account in order to nd hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be speci ed a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Modeling Social Annotation: A Bayesian Approach

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2010 by ACM Inc.
ISSN
1556-4681
DOI
10.1145/1870096.1870100
Publisher site
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Abstract

Modeling Social Annotation: A Bayesian Approach ANON PLANGPRASOPCHOK National Electronics and Computer Technology Center and KRISTINA LERMAN USC Information Sciences Institute Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, for example, Web pages or scienti c papers, with descriptive labels called tags. The social annotations contributed by thousands of users can potentially be used to infer categorical knowledge, classify documents, or recommend new relevant information. Traditional text inference methods do not make the best use of social annotation, since they do not take into account variations in individual users ™ perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes the interests of individual annotators into account in order to nd hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be speci ed a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery

Published: Dec 1, 2010

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