Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
On convergence of the EM algorithmand the Gibbs sampler
Statistics and Computing
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
The Journal of Machine Learning Research
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Usage patterns of collaborative tagging systems
Journal of Information Science
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
A study of mixture models for collaborative filtering
Information Retrieval
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Towards effective browsing of large scale social annotations
Proceedings of the 16th international conference on World Wide Web
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring social annotations for information retrieval
Proceedings of the 17th international conference on World Wide Web
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Operations for learning with graphical models
Journal of Artificial Intelligence Research
Automatically Constructing Semantic Web Services from Online Sources
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
LA-LDA: a limited attention topic model for social recommendation
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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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 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.