Difference between revisions of "Rosen-Zvi et al UAI 2004"
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This paper presents three aspects in the evaluation: | This paper presents three aspects in the evaluation: | ||
| − | - qualitatively show topics and author interests modeled from the two | + | - qualitatively show topics and author interests modeled from the two corpora |
- compare the [[Perplexity]] (predictive power) of the author-topic model with [[LDA]] | - 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 | - reveal potential applications of the author-topic model in author similarity computation and reviewer recommendation | ||
== Discussion == | == Discussion == | ||
| − | + | My views on this paper are: | |
| − | + | * This 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 papers, 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 == | == Related papers == | ||
Latest revision as of 22:22, 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 corpora - 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
My views on this paper are:
- This 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 papers, 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.
