Research Article Recommendation System Using LDA Topic Modeling

Abstract

"A personalised recommender system for research articles suggest research papers according to the user preferences. Many times user wants the suggestions according to his/her own interest. In this research article recommender system user gives the preferences of his/her domain and the system improves as he/she select the research articles. The system uses wikipedia documents to train basic models for twelve different domains. The topic modeling technique is used to define the topic of a given article. The wikipedia documents generate the trained model using Latent Dirichlet allocation (LDA), eighty topics for each domain. After generating these LDA models for all domains, system assigns a topic to the each significant word in every research article in the research article index, called a topic sequence. The topic sequencer generates the topic sequence for the top three most favourable domains, using domain selector. Domain selector use the probability of the specific word in LDA and select the topic of the highest probability for that word in the a specific domain."