A Service-Oriented Human Capital Management Recommendation Platform
Published in IEEE International System Conference (SysCon), 2019
Human Capital Management (HCM) involves various processes in finding the right talent for job openings, or finding satisfying jobs for employees in the service industry. With the arrival of automation and the help of Machine Learning (ML), models of complete or partial automation of HCM processes become more achievable. Such models become a necessity as organizations scale up. In this paper, we present a stateless, scalable, micro-service based architecture to push towards automation through recommendation using Machine Learning and Statistical Methods. A recommendation system for candidates in finding jobs, and vice versa, is presented. A Profile Score is allotted to a candidate/job and used as the metric for ranking. We show solutions to engineering challenges for such systems, like evaluating the location, educational and professional background, and career interests.