Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Expertise modeling for matching papers with reviewers
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Latent interest-topic model: finding the causal relationships behind dyadic data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploiting explicit semantics-based grouping for author interest finding
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
User-sentiment topic model: refining user's topics with sentiment information
Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
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This paper presents a hierarchical topic model that simultaneously captures topics and author's interests. Our proposal, the Author Interest Topic model (AIT), introduces a latent variable with a separate probability distribution over topics into each document. Experiments on a research paper corpus show that the AIT is useful as a generative model.