A latent topic model for linked documents

Authors:
Zhen Guo;Shenghuo Zhu;Yun Chi;Zhongfei Zhang;Yihong Gong
Affiliations:
State University of New York at Binghamton, Binghamton, NY, USA;NEC Laboratories America, Inc., Cupertino, CA, USA;NEC Laboratories America, Inc., Cupertino, CA, USA;State University of New York at Binghamton, Binghamton, NY, USA;NEC Laboratories America, Inc., Cupertino, CA, USA
Venue:
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Year:
2009

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Abstract

Documents in many corpora, such as digital libraries and webpages, contain both content and link information. To explicitly consider the document relations represented by links, in this paper we propose a citation-topic (CT) model which assumes a probabilistic generative process for corpora. In the CT model a given document is modeled as a mixture of a set of topic distributions, each of which is borrowed (cited) from a document that is related to the given document. Moreover, the CT model contains a random process for selecting the related documents according to the structure of the generative model determined by links and therefore, the transitivity of the relations among documents is captured. We apply the CT model on the document clustering task and the experimental comparisons against several state-of-the-art approaches demonstrate very promising performances.