Difference between revisions of "Rosen-Zvi et al UAI 2004"
| Line 11: | Line 11: | ||
== Summary == | == 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 [[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 [[UsesDataset::NIPS]] and [[UsesDataset::CiteSeer]] datasets. |
== Evaluation == | == Evaluation == | ||
| Line 17: | Line 17: | ||
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 corpus | - qualitatively show topics and author interests modeled from the two corpus | ||
| − | - compare the | + | - 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 == | ||
| − | - | + | - 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 == | ||
| + | |||
| + | - [http://www.cs.umass.edu/~mccallum/papers/art04tr.pdf 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. | ||
| + | - [http://www.datalab.uci.edu/author-topic/398.pdf Citation Author Topic Model in Expert Search] exploits citations to model author interests and perform expert search. | ||
Revision as of 23:14, 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
- 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
- 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.
