  Collaborative tagging systems, such as Delicious, CiteULike, and others,
allow users to annotate resources, e.g., 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 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 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 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 synthetic and real-world
data by comparing its performance to Latent Dirichlet Allocation on the topic
extraction task. For the latter evaluation, we apply the model to infer topics
of Web resources from social annotations obtained from Delicious in order to
discover new resources similar to a specified one. Our empirical results
demonstrate that the proposed model is a promising method for exploiting social
knowledge contained in user-generated annotations.
