Innovations in Campus Recruitment System: A Comprehensive Survey

Innovations in Campus Recruitment System: A Comprehensive Survey

Anjana Mano1, Tess Maria Johny2, Sandra Shaji3, Geethika S4, Shandry K K5,

College of Engineering Kidangoor, Kottayam, Kerala, India

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Abstract
Traditional recruitment practices rely on manual processes and keyword-based systems, leading to inefficiencies in processing large volumes of applications. Modern hiring platforms incorporate technologies such as machine learning (ML), hybrid recommendation algorithms, and natural language processing (NLP) to improve candidate-job matching, expedite processes, and enhance precision. These systems provide automatic evaluation, skill-based filtering, real-time alerts, and semantic matching. Furthermore, institutions can utilize centralized portals to streamline administrative tasks, fostering collaboration between students, employers, and administrators. Despite challenges such as dataset bias and the need for algorithmic refinement, these innovations enhance workforce readiness, align education with industry demands, and improve hiring efficiency.

Key Words

Campus Recruitment, Online Recruitment, Machine Learning, NLP, Job Matching, Skill Development, Hybrid Algorithms