Empowering Research Collaboration: AI-Enhanced Decision-Making through TF-IDF and Neo4j for Author Recommendations

Abstract

Collaboration among researchers is the backbone of scientific progress. Building strong connections among authors within the same research domain is paramount to achieving breakthroughs. In this article, we delve into a novel approach by amalgamating the TF-IDF (Term Frequency-Inverse Document Frequency) method with Neo4j, a powerful graph database, to develop a content-based recommendation system for authors. Our primary goal is to offer precise author recommendations to researchers working in similar fields, thereby facilitating collaboration and accelerating research advancement.