CareerBoost: Revolutionizing the Job Search with Resume Enhancement and Tailored Recommendations

Asoke Nath, Sunayana Saha, Shrestha Dey Sarkar and Anchita Bose

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 3, 281-290

Abstract: The Resume Enhancer and Job Recommendation System is designed to meet the unique challenges faced by job seekers in today's dynamic job market. Leveraging cutting-edge natural language processing (NLP) techniques, the present system provides a tailored solution to streamline the job search process. The present Resume Enhancer component utilizes advanced NLP algorithms to analyse resumes and job descriptions, generating comprehensive eligibility scores and targeted skill recommendations. This ensures that candidates' resumes are optimized to effectively showcase their qualifications and expertise to potential employers. The present Job Recommendation feature delivers personalized job listings tailored to each user's selected roles or career aspirations. The authors implemented machine learning algorithms such as the Random Forest Classifier and K-means Clustering, the system matches candidate preferences and qualifications with relevant job opportunities, increasing the likelihood of finding the perfect fit. Overall, the Resume Enhancer and Job Recommendation System serves as a valuable tool for job seekers, empowering them to navigate the complexities of the modern job market with confidence. With its user-centric approach and advanced technology, the present system enhances employability and facilitates career growth for individuals at every stage of their professional journey.

Keywords: Machine Learning; Random Forest Classifier; K-Means Clustering; Job Search; Skills Recommendation (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24103106
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i3:id:202

DOI: 10.32628/CSEIT24103106

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