HirePrep: An AI-Powered Resume Analyzer and Skill Gap Suggestion System

The emergence of online job portals has led to an exponential rise in the number of resumes being submitted for a given job posting.This has made manual resume screening inefficient, time-consuming, and prone to biases.To overcome these issues, there has been an increasing trend in the use of automated resume analysis systems based on Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques.This survey paper provides a comprehensive and systematic review of the existing literature on resume parsing, resume-job matching, and skill gap analysis.It traces the development of resume analysis techniques from traditional keyword-based and rule-based methods to conventional machine learning models and more recent transformer-based models like BERT.The survey critically evaluates the strengths and weaknesses of these techniques in terms of accuracy, scalability, interpretability, and generalizability.In addition, it also points out the research gaps in the existing literature, such as the need for explainable models, the need for standardized datasets, and the difficulties in dealing with domain diversity and biases.The results of this survey are expected to help in shaping the future research directions for the development of effective, scalable, and interpretable automated recruitment systems.