Artificial Intelligence-Based Job Applicant Profile Quality Assessment and Relevance Ranking Using Clusters in Talent Acquisition Process

Abstract

The traditional way of hiring has become a less competitive, more costly, and time-consuming process in the current labor market. Artificial Intelligence (AI) has brought a radical change in human resource management. The number of applicants to apply for a job has increased by many folds than earlier. This article mainly focuses on designing a model that scrutinizes the candidate profiles in the database according to job descriptions. An AI model has been developed to assess the quality and candidate profile selection. The model performance preprocessing then creates the clusters of words using Natural Language Processing (NLP). The distance between the clusters indicates the closeness between the job description and candidate profile. The quality measure is then computed based on distance measurement and clusters. Using quality measure, the candidate profiles are selected which are relevant to the job description. This reduces the cost of hiring and also saves time in the process. Another important aspect is the unbiased hiring decision and fewer manual errors.