Recommendation systems for recruitment within an educational context

Abstract: Alongside the evolution of the recruitment process, different types of recommendation systems have been developed. The purpose of this study is to investigate recommendation systems within educational contexts, successful implementations of recommendation system architecture patterns, and alternatives to previous experience when evaluating candidates. The study is conducted through two separate methods; A literature review with a qualitative approach and design science research methodology focused on design and development, demonstration and evaluation. The literature review shows that, for recommendation systems, a layered architecture built within a microservice ecosystem is successfully utilized and has multiple beneficial aspects such as improved scalability, maintainability and security. Through design science research methodology, this study shows a suggested approach to implementing a layered architecture in combination with KNN and hybrid filtering. To avoid the lapse of suitable candidates, caused by demanding previous experience, this study shows an alternative approach to recruitment, within an educational context, through the use of soft skills. Within the study, this approach is successfully used to evaluate and compare students, but the same approach could possibly be applied to evaluate and compare companies. Moving forward, this study could be further expanded by looking into possible biases arising as a result of using AI and choices made during this study, as well as weighting of student-attributes.