Abstract. The boost in the volume of digital academic repository made it difficile for the research scholars to get to the kindred academic articles subsequently driving the scrutiny of usage of Recommender Systems (RS) in academic literature domain. The techniques, content or collaborative filtering based, cannot placate the scholar’s personalized preferences relating to recentness of the date of publishing, popularity of the article etc. The following work presents an Enhanced Approach for Recommending Research Articles using the Technique of High-Utility Itemset Mining (HUIM). The approach considers both the content of research article and scholar’s personalized preference before recommending papers. For this, an HUIM algorithm, FHM, is employed to pick the articles that have higher utility from the user’s perspective. Analysis results verify that this approach contemplates research scholar’s personalized preferences and at the same point surpasses the existing approaches by requiring less time and space.