Difference between revisions of "Rosen-Zvi et al UAI 2004"
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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. | 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. | ||
| − | + | == Evaluation == | |
| + | |||
| + | This paper presents three aspects in the evaluation: | ||
| + | - qualitatively show topics and author interests modeled from the two corpus | ||
| + | - compare the perplexity (predictive power) of the author-topic model with LDA | ||
| + | - reveal potential applications of the author-topic model in author similarity computation and reviewer recommendation | ||
| + | |||
| + | == Discussion == | ||
| + | |||
| + | - | ||
Revision as of 21:46, 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.
Evaluation
This paper presents three aspects in the evaluation:
- qualitatively show topics and author interests modeled from the two corpus - compare the perplexity (predictive power) of the author-topic model with LDA - reveal potential applications of the author-topic model in author similarity computation and reviewer recommendation
Discussion
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