Rosen-Zvi et al UAI 2004

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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