Abstract: In the field of Information Filtering we have many term-based or pattern –based methods for generating user’s needed information from a set of documents .A general thinking is that documents in a particular collection is related to only a single topic. But in real life user’s interest is different and documents in a set or collection includes multiple topics. Most commonly used topic modelling method is Latent Dirichlet Allocation (LDA) which generates a structural model to represent multiple topics in a set of documents. Patterns generally are more descriptive and efficiently used in real time applications. So to select most descriptive and efficient patterns from the discovered set of patterns here a Maximum matched Pattern-based Topic Model is introduced. It helps us to get the relevant document according to user needs by filtering out unwanted documents.
Keywords: Topic Model, Information Filtering, Pattern mining, relevance ranking, user interest model.
Title: Pattern Enhanced Topic Model
Author: Tincy Chinnu Varghese, Smitha C Thomas
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Research Publish Journals
