  We introduce the author-topic model, a generative model for documents that
extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include
authorship information. Each author is associated with a multinomial
distribution over topics and each topic is associated with a multinomial
distribution over words. A document with multiple authors is modeled as a
distribution over topics that is a mixture of the distributions associated with
the authors. We apply the model to a collection of 1,700 NIPS conference papers
and 160,000 CiteSeer abstracts. Exact inference is intractable for these
datasets and we use Gibbs sampling to estimate the topic and author
distributions. We compare the performance with two other generative models for
documents, which are special cases of the author-topic model: LDA (a topic
model) and a simple author model in which each author is associated with a
distribution over words rather than a distribution over topics. We show topics
recovered by the author-topic model, and demonstrate applications to computing
similarity between authors and entropy of author output.
