A Comprehensive Review Of Campus Recruitment Systems Using Machine Learning

Abstract

Campus recruitment systems play a vital role in bridging the gap between educational institutions and the job market. As the hiring process becomes increasingly competitive and data-driven, leveraging Machine Learning (ML) techniques in campus recruitment has emerged as an innovative solution to streamline the selection process, reduce biases, and improve decision-making. This paper provides a comprehensive review of the integration of ML in campus recruitment systems, exploring various ML algorithms and their applications, such as predictive analytics for candidate selection, resume screening, job matching, and interview performance analysis. The paper also examines the benefits, challenges, and limitations of ML in recruitment, such as data privacy concerns, algorithmic bias, and the need for large datasets. Additionally, it highlights the emerging trends, including natural language processing (NLP) for better understanding resumes and candidate interactions, as well as AI-powered chatbots for improving candidate engagement. By reviewing existing systems and case studies, this paper aims to present a holistic understanding of how ML can enhance the effectiveness and efficiency of campus recruitment processes, offering insights for future research and implementation in this domain.