Accurately representing the quantity and characteristics of users' interest in certain topics is
an important problem facing topic evolution researchers, particularly as it applies to modern …

Smart communities are composed of groups, organizations, and individuals who share
information and make use of that shared information for better decision making. Shared …

We propose a novel interpretation of the collapsed variational Bayes inference with a zero-
order Taylor expansion approximation, called CVB0 inference, for latent Dirichlet allocation …

Social media has become an important source of near-instantaneous information about
events and is increasingly also being analysed to provide predictive models, sentiment …

We propose a new topic model for analyzing purchase data with price information. Price is
an important factor in consumer purchase behavior. The proposed model assumes that a …

Humans form concept of objects by classifying them into categories, and acquire language
by simultaneously interacting with others. Thus, the meaning of a word can be learned by …

We propose a novel collapsed variational Bayes (CVB) inference for the hierarchical
Dirichlet process (HDP). While the existing CVB inference for the HDP variant of latent …

Many articles in the online encyclopedia Wikipedia have hyperlinks to ambiguous article
titles; these ambiguous links should be replaced with links to unambiguous articles, a …

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, some …

We study parameter inference in large-scale latent variable models. We first propose a
unified treatment of online inference for latent variable models from a non-canonical …