Maximum Matched Pattern-based Topics for Document Modeling in Information Filtering

Abstract: In the field of Information Filtering we have many term-based or pattern �based methods for generating user�s needed form information from a set of documents .A basic general thinking is that documents in a set of 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 modeling 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.