Name
Abstract | ||
|---|---|---|
This paper presents a hierarchical generative model that captures the latent relation of cause and effect underlying user behavioral-originated data such as papers, twitter and purchase history. Our proposel, the Latent Interest Topic model (LIT), introduces a latent variable into each document and each author layor in a coherent generative model. We call the former variable the document class, and the latter variable the author class, where these classes are indicator variables that allow the inclusion of different types of probability, and can be shared over documents with similar content and authors with similar interests, respectively. Significantly, unlike other works, LIT differentiates, respectively, document topics and user interests by using these classes. Consequently, LIT is superior to previous models in explaining the causal relationships behind the data by merging similar distributions; it also makes the computation process easier. Experiments on a research paper corpus show that the proposed model can well capture document and author classes, and reduce the dimensionality of documents to a low-dimensional author-document space, making it useful as a generative model. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1871437.1871521 | CIKM |
Keywords | Field | DocType |
document class,document topic,lit differentiates,hierarchical generative model,previous model,causal relationship,dyadic data,author class,latent interest-topic model,latent interest topic model,coherent generative model,generative model,graphical model,latent variable modeling,information extraction,topic modeling,graphical models,latent variable,latent variable model | Data mining,Latent Dirichlet allocation,Computer science,Latent class model,Latent variable,Probabilistic latent semantic analysis,Artificial intelligence,Natural language processing,Information retrieval,Latent variable model,Graphical model,Topic model,Generative model | Conference |
Citations | PageRank | References |
9 | 0.63 | 15 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noriaki Kawamae | 1 | 119 | 10.96 |