(HR)^2: An Agent for Helping HR with Recruitment

Abstract

Finding the right candidate for a job has always been a hard task that Human Resources HR managers of a company face regularly. In this paper, the authors propose that the field of multi-agents can play a significant role in a elaborating the job description b getting an applicant to submit competencies relevant to the job c shortlisting applicants and d identifying the right hire. They propose the model of HR^2, an automated agent for Helping HR with Recruitment that could perform the following key steps: a Generate Specific Position Contract SPC from a Master Position Contract MPC using Infer1 procedure b Use the SPC to provide a graded and iterative feedback to applicant using Infer2 procedure. They situate HR^2 in the context of LinkedIn. To enable better inference, they propose to modify the information being collected by LinkedIn, using the ontology provided by the free online database O*NET. The HR^2 agent will be able to help the employer rank order the SPCs and identify areas for assessment, potentially easing the interview process and leading to high quality hires.