Resume Ranking with TF-IDF, Cosine Similarity and Named Entity Recognition

Resume ranking algorithms have become a vital tool in modern workforce management, as they help applicants face almost insurmountable odds of ever scoring an interview. Natural language processing (NLP) and machine learning algorithms are used to assess a candidate's resume and sort them on the basis with which he/she is compatible with the specific job description. This paper analyses many resume ranking methods with an emphasis on the Term Frequency-Inverse Document Frequency (TF-IDF) and cosine Similarity. All these methods help us refine the ranking system for resumes and, hence, better relevant scores. In addition, Named Entity Recognition (NER) improves the possibility of detecting key entities, including skills and experience, to lead to a better ranking system. This also had the edge case where traditional ranking approaches failed to respond with limited functionality as they were limited to shallow keyword matching and provided only a summary of candidates' profiles. It addresses the problem of resume ranking competition and presents three other critical issues: bias, transparency and data privacy. Finally, it evaluates the ability of the system to detect and track against conventional methods. It also sets a path for future research in the field of business and to find out how efficient these resume screening automated systems.