Latent interest-topic model: finding the causal relationships behind dyadic data

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.