In this paper, we propose a new probabilistic generative model, called Topic-Perspective Model, for simulating the generation process of social annotations. Different from other g...
Caimei Lu, Xiaohua Hu, Xin Chen, Jung-ran Park, Ti...
—It is well known that Web users create links with different intentions. However, a key question, which is not well studied, is how to categorize the links and how to quantify th...
Jie Tang, Jing Zhang, Jeffrey Xu Yu, Zi Yang, Keke...
We propose an online topic model for sequentially analyzing the time evolution of topics in document collections. Topics naturally evolve with multiple timescales. For example, so...
We propose a new unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a...
Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L...
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...