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PLACEMENT AND RESUME MATCHER

Naveen K, Mrs. Deepthi Nair P, Nandhini K, Nandhini K, Nihal N

Abstract


This paper presents a Placement and Resume Matcher system developed to automate resume screening and improve candidate-job matching for campus placement processes. Traditional recruitment systems often depend on manual resume evaluation or simple keyword-based filtering, which can be inefficient, time-consuming, and less accurate in identifying relevant candidate skills. The proposed system applies Natural Language Processing (NLP) techniques to extract meaningful information from unstructured resume documents. A custom Named Entity Recognition (NER) model built using spaCy is used to identify and classify skill-related entities from resumes. An Applicant Tracking System (ATS)-based scoring mechanism compares extracted candidate skills with job requirements to calculate an effective match score. The system is integrated with a Streamlit-based user interface, enabling users to upload resumes and receive real-time analysis and matching results. This solution enhances recruitment efficiency, reduces manual workload, and provides a scalable and intelligent approach for modern placement management systems.


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References


. Brown, T.; Green, A. (2024). Artificial Intelligence Applications in Smart Placement and Recruitment Systems. Journal of Emerging Employment Technologies, Vol. 12(1), pp. 67–91

. Glassdoor Economic Research (2024). AI-Driven Job Recommendation and Career Matching Technologies. Employment Innovation Review, pp. 50–69.

. IBM Watson AI Research (2023). Intelligent Resume Screening and Candidate Ranking Systems. AI Recruitment Journal, Vol. 9(1), pp. 55–73.

. Indeed Hiring Lab (2023). Resume Parsing and Job Recommendation Technologies for Enhanced Candidate Matching. Recruitment Technology Journal, pp. 45–58.

. IEEE Research Publications (2023). Automated Skill Matching and Candidate Selection Using Artificial Intelligence. IEEE Transactions on Smart Recruitment, Vol. 5(4), pp. 210–228.

. JSON Technical Standards Group (2024). Standardized Skill Databases for Resume Skill Normalization. Software Engineering Standards Review, pp. 18–33.

. Kumar, R.; Sharma, P. (2023). Machine Learning Approaches for Resume Classification and Candidate Recommendation. International Journal of AI Recruitment Systems, Vol. 8(2), pp. 101–120.

. LinkedIn Corporation (2024). AI-Powered Resume Screening and Talent Matching Systems in Modern Recruitment Platforms. Industry Research Publications, pp. 12–25

. Microsoft Research (2024). Artificial Intelligence in Talent Acquisition and Workforce Recruitment. Digital Hiring Systems Review, pp. 80–102.

. OpenAI Research Team (2024). Large Language Models for Resume Understanding r4and Automated Recruitment. Advanced AI Applications Journal, pp. 130–150.


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