BERT- and FastText-Based Research Paper Recommender System

Abstract

With the advent of digital libraries, researchers can now access a plethora of publications and journals from any part of the world. These research papers play an instrumental role in acquainting researchers with the latest technological advancements taking place all over the world. Students who wish to know about the latest technologies also peruse these research papers. But currently, there is a dearth of satisfactory approaches for getting relevant recommendations. In the era of digital libraries, the importance of research paper recommendations is increasing day by day. However, there is a paucity of recommendation systems that allows us to leverage these information sources effectively. This places some limitations on an application that has great potential. So, a research paper recommendation system that allows us to generate accurate predictions is exigent. As abstracts are representative of the whole research paper, a research paper recommendation system that generates relevant keywords out of the abstract using BERT and provides recommendations would be both quick as well as accurate. BERT embeddings for keyword extraction from the abstracts and FastText for classification of the words and sentences are used for better evaluation and recommendations of the research papers. This method will fetch better results in comparison to the traditional approaches used for keyword extraction as well as research paper recommendation.