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

Authors

  • Asoke Nath Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, India Author
  • Sunayana Saha Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, India Author
  • Shrestha Dey Sarkar Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, India Author
  • Anchita Bose Department of Computer Science, St. Xavier’s College (Autonomous), Kolkata, India Author

DOI:

https://doi.org/10.32628/CSEIT24103106

Keywords:

Machine Learning, Random Forest Classifier, K-Means Clustering, Job Search, Skills Recommendation

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.

Downloads

Download data is not yet available.

References

Niti Khamker, Yuti Khamker, Mani Butwall, “Resume Match System”, International Journal of Innovative Science and Research Technology ISSN No:-2456-2165.

Wenxing Hong, Siting Zheng, Huan Wang, Jianchao Shi, “A Job Recommender System Based On User Clustering”, JOURNAL OF COMPUTERS, VOL. 8, NO. 8, AUGUST 2013. DOI: https://doi.org/10.4304/jcp.8.8.1960-1967

S. T. Al-Otaibi and M. Ykhlef, “A survey of job recommender systems,” International Journal of the Physical Sciences, vol. 7(29), pp. 5127-5142, July, 2012. [4] A. Singh, C. Rose, K. Visweswariah, V. Chenthamarakshan and N. Kambhatla, “PROSPECT: a system for screening candidates for recruitment,” In Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 659-668, Toronto, Canada, 2010.

F. Färber, T. Weitzel and T. Keim, “An automated recommendation approach to selection in personnel recruitment,” In Proceedings of the 2003 Americas Conference on Information Systems, pp. 2329-2339, Tampa, USA, 2003

Ujjal Marjit, Kumar Sharma and Utpal Biswas, “Discovering resume information using linked data”, International journal of web & semantic technology, Vol.3, No. 2, 2012. DOI: https://doi.org/10.5121/ijwest.2012.3204

Divya Mule, Samiksha Doke ,Sakshi Navale, Prof. S.K.Said, RESUME SCREENING USING LSTM, International Research Journal of Engineering and Technology (IRJET) eISSN: 2395-0056 Volume: 09 Issue: 04 | Apr 2022 www.irjet.net p-ISSN: 2395-0072.

Downloads

Published

19-05-2024

Issue

Section

Research Articles

Similar Articles

1-10 of 264

You may also start an advanced similarity search for this article.