An Integrated Approach to Research Paper and Expertise Recommendation in Academic Research

Research papers and expertise recommendation systems have been operating independently of one another, resulting in avoidable search queries and unsatisfactory recommendations. This study developed an integrated recommendation system to harmonize research papers and expertise recommendations in the academic domain for enhanced research collaboration. It aims to address issues related to the isolated existence of these systems. A recommendation algorithm was developed to synergize the research paper and expertise recommendation models. The Cosine similarity function between user query and available research papers as well as experts, was combined with selected criteria to achieve recommendation. The synergized model was simulated and evaluated using Precision, Recall, F-measure and Root Mean Square Error as performance metrics. The findings showed that the harmonization of research paper and expertise recommendation approaches provides a holistic and enhanced approach towards research paper and expertise recommendation. Thus, academic researchers now have a reliable way to recommend experts and research papers, which will lead to more collaborative research activities.