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

- I think this paper is a natural (but smart) extension of the popular LDA model. It inspired a bunch of following works including the author-recipient-topic model. - Modeling author/user interests in the form of topic distributions is very useful in practice. For example, we can recommend news or papers to users based on their interests. Also, expert/expertise search can be improved by exploiting user interest information. - Compared with scientific paper, social media has much more information such as links, user groups and interaction patterns. The author-topic model can be extended to incorporate these information and model user interests/preferences in social media services.

Related papers

Here are two papers related with this work.

• The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks is directly based on LDA and the author-topic model. It models links (interactions) in social networks, where each link is conditioned on the sender and recipient.
• Citation Author Topic Model in Expert Search exploits citations to model author interests and perform expert search.