2Q-Learning Scheme for Resume Screening

Resume screening is a tedious process as it involves a lot of challenges in terms of handling a voluminous number of resumes, performing screening operations, extracting precisely suitable competent profiles out of all available profiles, and so on. In literature several techniques are available for resume screening which include manual inspection, resume parsing, and many more. These techniques produce inconsistent results and consume a lot of time of recruiters as all the techniques lack automation and learning capabilities. This paper uses a 2Q-Learning-based approach enriched with natural language processing capability to accurately evaluate resumes. Additionally, by offering advice on potential resume improvements, it helps to enrich the candidate’s resume. It also eliminates the unconscious prejudice and human errors that occur throughout the traditional recruitment procedure. Overall, it is advantageous for both job seekers and recruiters in terms of learning rate, scalability, and accuracy. Overall, it is advantageous for both job seekers and recruiters.