Personalized research paper recommender system

Abstract

Personalization is an emerging topic in the field of Research paper recommender systems and academic research. It is a technique to creative and efficient user profiles to achieve improved recommendations. Our work proposes a new user model to understand user behavior for personalization. This model initially extracts keywords based on the online behaviour of the user. The subsequent steps include concept extraction and user profile ontology construction to derive inferences and define relationships. The suggested model clearly depicts hierarchical ordering of the user’s long-term and current research interests. Furthermore, the adoption of our model contributes to improvement of recommendations.