Youngchul Cha

Learn More
In this paper, we discuss how we can extend probabilistic topic models to analyze the relationship graph of popular social-network data, so that we can group or label the edges and nodes in the graph based on their topic similarity. In particular, we first apply the well-known Latent Dirichlet Allocation (LDA) model and its existing variants to the(More)
Topic models are used to group words in a text dataset into a set of relevant topics. Unfortunately, when a few words frequently appear in a dataset, the topic groups identified by topic models become noisy because these frequent words repeatedly appear in "irrelevant" topic groups. This noise has not been a serious problem in a text dataset because the(More)
Categorical (topic) similarity between a web page and an advertisement (ad) text has long been used for contextual advertising. In this paper, we explore the use of the categorical similarity score, referred to as Category Match Score (CMS), in the context of search advertising. In particular, we explore the effect of CMS on various ad-effectiveness(More)
Probabilistic topic models, such as PLSA and LDA, are gaining popularity in many fields due to their high-quality results. Unfortunately, existing topic models suffer from two drawbacks: (1) model complexity and (2) disjoint topic groups. That is, when a topic model involves multiple entities (such as authors, papers, conferences, and institutions) and they(More)
  • 1