AI-Based Job Recommendation System Using Skill Extract & Matching

Abstract

The conventional job search process tends to be disjointed, leaving job seekers confused by non-relevant job postings and hiring managers drowning in the manual processing of resumes. This study proposes the concept of an AI-Enabled Job Portal, an all-encompassing platform that aims to close the gap between qualified individuals and available opportunities through Intelligent Matching Algorithms and Natural Language Processing (NLP). Unlike conventional job portals, this platform includes an integrated AI-powered resume parser, job recommendation engines, and a centralized dashboard for both job seekers and employers. Through the application of semantic intent as opposed to keyword searching, the portal guarantees improved resume placement accuracy and a faster hiring process.

Introduction

The digital job market has created an abundance of job information, but finding the right fit remains challenging. Traditional portals are passive, relying on manual filtering, which often leads to inefficiency and subjective bias. The proposed Intelligent AI-Enabled Job Portal addresses these limitations by actively evaluating candidate profiles against job requirements using Generative AI, NLP, and scoring systems.

Literature Survey:

• Early recruitment relied on Boolean searches and manual classification, lacking contextual understanding.

• Machine learning improved candidate ranking but often functioned as opaque black-box systems.

• NLP models like BERT enhance semantic understanding between resumes and job descriptions.

• Large Language Models (LLMs) enable deeper skill extraction and intelligent candidate evaluation.

• Modern web frameworks like React facilitate interactive dashboards for recruiters, improving management efficiency.

Problem Statement:
Traditional recruitment platforms fail to normalize academic credentials or verify practical skills (e.g., GitHub projects), creating trust issues. The system proposes a transparent, skill-based AI ecosystem for IT and medical recruitment.

System Architecture:

• Centralized AI-enabled portal connecting students and recruiters.

• Modules include user authentication, profile/job management, application processing, and an AI-based matching engine.

• The matching engine generates Job-Match Scores to recommend the most relevant jobs to candidates and rank candidates for recruiters.

Methodology:

1. Data Collection & Integration: Collect structured data from recruiters (job descriptions) and candidates (resumes, skills, projects, internships).

2. Resume & Job Processing: Use NLP techniques (cleaning, tokenization, lemmatization) to extract meaningful skills, experience, and education.

3. Skill Verification & Profile Evaluation: Validate technical expertise via GitHub repositories, normalize academic scores, and assess project/internship experience.

4. Job Matching & Recommendation: Calculate Job-Match Scores for automated candidate-job alignment, improving recruitment efficiency and reducing manual effort.

Technology Stack:

• Frontend: React.js

• Backend: Node.js + Express.js

• Database: MongoDB

• NLP: Python (NLTK, Scikit-learn)

• Version Control: Git/GitHub

• Deployment: Cloud platforms (Render, Vercel)

Input & Result:

• Recruiter dashboard allows managing company information, adding/updating profiles, and searching/filtering companies efficiently.

• The AI system provides automated job recommendations to candidates and ranked candidate lists to recruiters, enhancing transparency and matching accuracy.

Conclusion

The AI-enabled job portal was successfully developed to provide skill-based job recommendations and intelligent candidate matching. The system uses Artificial Intelligence and Natural Language Processing techniques to analyze resumes and match candidates with suitable jobs. It reduces manual effort, improves recruitment efficiency, and provides accurate job recommendations. The proposed system helps both recruiters and job seekers by making the hiring process faster, easier, and more effective.

References

[1] R. Kumar and S. Varma, \"AI-Driven Recruitment Systems using MERN Stack and NLP,\" IEEE Access, vol. 12, pp. 4567–4578, 2024. [2] A. Sharma et al., \"Semantic Matching and Candidate Ranking in Modern ATS,\" International Journal of Computer Applications, vol. 180, no. 15, pp. 12–19, 2023. [3] J. Devlin et al., \"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,\" Proceedings of NAACL, 2019. [4] MongoDB Inc., \"NoSQL Database Design for Scalable Recruitment Platforms,\" Technical Documentation, 2024. [5] OpenAI, \"GPT-4 Technical Report: Contextual Analysis and Reasoning,\" Technical White Paper, 2023. [6] React Community, \"Building High-Performance Dashboards for Real-time Analytics,\" Official Documentation, 2024.

Copyright

Copyright © 2026 G. Yogeswara Rao, G. Navadeep , M. Gahindra , N. Dhara Durga Prasad, V. Krishnamraju. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.