Autoapply and Career Guide System

Abstract

The difficulties of traversing many platforms, entering information repeatedly, and manually monitoring the status of their job applications are present for job seekers in the digital age. In order to solve these inefficiencies, the Auto Apply and Career Guide System provides a cohesive and clever way to automate the job search procedure. A web-based system that allows for one-click job applications, real-time status tracking, and customized career path recommendations is designed and developed in this article. It uses cosine similarity for resume–job match scoring and Natural Language Processing (NLP) approaches to compare resume content with job requirements, hence incorporating skill gap analysis. Additionally, the system offers recommendations for specific learning materials to close identified gaps and improve employability. React.js was used for the front end, and Node.js and Express.js for the backend, Using MongoDB for data administration, the platform prioritizes data protection, scalability, and user experience.

Introduction

Problem Statement:

Job seekers face challenges in the digital job market due to:

• Repetitive data entry,

• Fragmented application tracking,

• Lack of personalized career guidance,

• Mismatch between job requirements and applicant skills.

Proposed Solution:

Auto Apply and Career Guide System is a smart, user-centric platform that:

• Enables one-click job applications,

• Tracks application status in real time,

• Conducts skill gap analysis using NLP and similarity algorithms,

• Provides customized career recommendations and learning resources.

System Features:

• Frontend: Built with React.js, offering modules for skill feedback, dashboards, job applications, and user onboarding.

• Backend: Powered by Node.js and Express.js, handling resume parsing, skill comparison, and authentication.

• Database: Uses MongoDB to store user data, resumes, job postings, and skills.

Data Processing Pipeline:

1. Text Extraction: Resumes and job descriptions are parsed (including scanned images via OCR).

2. Text Preprocessing: Removes symbols, lowercases, and tokenizes content.

3. Skill Extraction: Compares cleaned text with a master list to extract skills.

4. Skill Gap Analysis: Identifies missing skills by comparing job requirements and resume content.

5. Resume–Job Match Scoring: Uses cosine similarity to calculate match percentage.

6. Skill Recommendations: Suggests relevant online courses (e.g., Coursera, Udemy) based on missing skills.

7. Dashboard Visualization: Displays skill match score, missing skills, and recommended resources with visual graphs.

Literature Review Highlights:

• Prior studies explored skill gap models, automated job systems, and career portals.

• Existing systems lacked real-time feedback, skill development integration, and comprehensive automation.

• The proposed system addresses these gaps with end-to-end automation and career guidance.

Results:

• Successfully integrates job application automation, skill gap analysis, and personalized learning in a unified platform.

• Enhances efficiency, usability, and job-readiness for users.

• Demonstrates significant qualitative improvement in the job application experience.

Conclusion

The Auto Apply and Career Guide System effectively streamlines the job application process through one-click apply, real-time status tracking, and personalized career guidance. By integrating resume parsing, skill gap analysis, and course recommendations, the system enhances both job search efficiency and user employability.In the future, the system can be expanded with features like AI-based resume building, mobile app support, real-time skill assessments, and smarter job matching through machine learning. These enhancements aim to make the platform more intelligent, accessible, and impactful for job seekers.

References

[1] Patel and M. Roy, \"Skill gap analysis using clustering algorithms for job matching,\" 2023 International Conference on Data Science and Applications (ICDSA), pp. 256–262, IEEE, 2023 [2] J. Lee and K. Park, \"Automated job application processing and recommendation framework,\" IEEE Transactions on Computational Social Systems, vol. 10, no. 2, pp. 123–130, Feb. 2023. [3] T. Nguyen, R. Singh, A. Banerjee, and M. Kim, \"A comprehensive survey on job portals and intelligent career planning tools,\" IEEE Reviews in Biomedical Engineering, early access, 2023. [4] M. Sharma and S. Dey, \"NLP techniques for resume and job description analysis: A review,\" International Journal of Computer Applications, vol. 182, no. 40, pp. 15–22, 2021. [5] S. Kumar and A. Verma, \"Resume-job matching system using cosine similarity,\" Journal of Intelligent Systems, vol. 31, no. 1, pp. 60–72, 2022.

Copyright

Copyright © 2025 M. Vineela, Biradar Poonam, Yekamba Prathyusha, G Spandana Angel. 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.