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论文阅读总结

Topic visualization

Visualizing Topic Models by Allison J.B. Chaney and David M. Blei（AAAI2012）

Probabilistic Latent Semantic Visualization: Topic Model for Visualizing Documents by Tomoharu Iwata, et al. （WWW2006）

Bayesian nonparametircs

A tutorial on Bayesian nonparametric models by Samuel J. Gershman and David M. Blei （Journal of Mathematical Psychology 2011）

The Infinite Gaussian Mixture Model by Carl Edward Rasmussen （NIPS2000）

Markov Chain Sampling Methods for Dirichlet Process Mixture Models by Radford M. Neal

Topic model application

Social-Network Analysis Using Topic Models by Youngchul Cha and Junghoo Cho （SIGIR2012）

 We use the same notation with LDA. $z$ denotes a labeling of a followed user $g$ with a topic (interest), or simply a topic, $P(z f)$ denotes the multinomial distribution of topics given a follower $f$, and $P(g z)$ denotes the multinomial distribution of followed users given a topic. $\alpha$ and $\beta$ are Dirichlet priors, constraining $P(z f)$ and $P(g z)$, respectively.

Modeling User Posting Behavior on Social Media by Zhiheng Xu, et al.（SIGIR2012）

• Breaking news
• posts from social friends
• users’ intrinsic interest 作者扩展了LDA模型，将这三种原因加入到模型中。这篇文章主要参考了另外两篇文章：Modeling General and Specific Aspects of Documents with a Probabilistic Topic Model（NIPS2006）和Cross-Cultural Analyisi of Blogs and Forums with Mixed-Collection Topic Models（EMNLP2009）。