Abstract

Collaborative tagging systems, such as Delicious, CiteULike, and others, allow users to annotate resources, for example, Web pages or scientific 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 find hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be specified a priori. To address this drawback, we extend the model to a fully Bayesian framework, which …

Date

December 1, 2010

Authors

Anon Plangprasopchok, Kristina Lerman

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)

Volume

5

Issue

1

Pages

1-32

Publisher

ACM