Difference between revisions of "Rosen-Zvi et al UAI 2004"
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| − | [www.datalab.uci.edu/author-topic/398.pdf The Author-Topic Model for Authors and Documents] | + | [http://www.datalab.uci.edu/author-topic/398.pdf The Author-Topic Model for Authors and Documents] |
== Summary == | == Summary == | ||
| − | This paper | + | This paper proposes the generative author-topic model, which extends LDA to model document contents and authors at the same time. The interest of each author is represented with a multinomial distribution over topics, while each topic is a multinomial distribution over words. Model estimation is performed with Gibbs sampling. Experiments show the topic-author and topic-word results discovered on the NIPS and [[UsesDataset::CiteSeer]] datasets. |
The experimented on 3 real world social graphs [[UsesDataset::Conference-Author dataset]], [[UsesDataset::Author-Paper dataset]] and [[UsesDataset::IMDB dataset]] | The experimented on 3 real world social graphs [[UsesDataset::Conference-Author dataset]], [[UsesDataset::Author-Paper dataset]] and [[UsesDataset::IMDB dataset]] | ||
Revision as of 22:35, 26 September 2012
This a Paper discussed in Social Media Analysis 10-802 in Fall 2012.
Citation
The Author-Topic Model for Authors and Documents. Michal Rosen-Zvi, Thomas Griffiths, Mark Steyvers, Padhraic Smyth. In Proceedings of UAI 2004, pages 487-494.
Online version
The Author-Topic Model for Authors and Documents
Summary
This paper proposes the generative author-topic model, which extends LDA to model document contents and authors at the same time. The interest of each author is represented with a multinomial distribution over topics, while each topic is a multinomial distribution over words. Model estimation is performed with Gibbs sampling. Experiments show the topic-author and topic-word results discovered on the NIPS and CiteSeer datasets.
The experimented on 3 real world social graphs Conference-Author dataset, Author-Paper dataset and IMDB dataset
