SkillLens- AI-Powered Skill Gap Analyzer for Job Seekers
Keywords:
AI career guidance, BERT, Career recommendation, Learning path, NLP, SBERT, Skill gap analysisAbstract
In today’s fast-changing job market, professionals often struggle to identify which skills they truly need to achieve their dream careers. Most job portals rely on keyword matching and fail to provide clear guidance on what skills are missing or how to improve them. To address this issue, this project presents an AI-powered Skill Gap Analyzer and Career Path Recommender that uses Natural Language Processing (NLP) and deep learning models. The system compares a user’s resume with the job description to identify missing or underdeveloped skills. It then suggests a personalized learning roadmap, including recommended courses, certifications, and a readiness score that reflects how well-prepared the user is for their target role. By combining semantic similarity analysis, entity extraction, and data visualization, the platform empowers individuals to take informed steps toward career growth.
References
C. C. Aggarwal, “An Introduction to Recommender Systems,” Recommender Systems, pp. 1–28, 2016, doi: https://doi.org/10.1007/978-3-319-29659-3_
S. Pabalkar, P. Patel, R. Choudhary, V. Panoch, S. Yadav, and H. Ghogale, “Resume Analyzer Using Natural Language Processing (NLP),” In2024 International Conference on Intelligent Systems and Advanced Applications, pp. 1–6, Oct. 2024, doi: https://doi.org/10.1109/icisaa62385.2024.10828940
X. Q. Ong and K. H. Lim, “SkillRec: A Data-Driven Approach to Job Skill Recommendation for Career Insights,” arXiv (Cornell University), pp. 40–44, Mar. 2023, doi: https://doi.org/10.1109/iccae56788.2023.10111438
K. Sanyal, “Intelligent Resume Parsing and Job Recommendation via Web-Based CV Analysis System,” International Journal for Research in Applied Science and Engineering Technology, vol. 13, no. 5, pp. 2291–2300, May 2025, doi: https://doi.org/10.22214/ijraset.2025.70746
A. K. Sinha, M. Amir, and M. Kumar, “Automated Resume Parsing and Job Domain Prediction using Machine Learning,” Indian Journal of Science and Technology, vol. 16, no. 26, pp. 1967–1974, Jul. 2023, doi: https://doi.org/10.17485/ijst/v16i26.880.
M. Liu, J. Wang, K. Abdelfatah, and M. Korayem, “Tripartite Vector Representations for Better Job Recommendation,” arXiv.org, 2019. https://arxiv.org/abs/1907.12379
A. Kulkarni, “Advancing Job Search: A Comprehensive Resume-Based Job Recommendation System Using NLP and Deep Learning Techniques,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, no. 2, pp. 1054–1058, Feb. 2024, doi: https://doi.org/10.22214/ijraset.2024.58505
Channabasamma and Y. Suresh, “Machine Learning-Based Recommendations and Classification System for Unstructured Resume Documents,” Revue D’intelligence Artificielle, vol. 37, no. 3, pp. 619–625, Jun. 2023, doi: https://doi.org/10.18280/ria.370311
M. Kashif and P. Kumar, “Resume Parser Using NLP,” IJARCCE International Journal of Advanced Research in Computer and Communication Engineering Impact Factor 8.102 Peer-reviewed & Refereed journal, vol. 13, no. 9, 2024, doi: https://doi.org/10.17148/ijarcce.2024.13905
Ch. Raja, R. Kumar, and S. Sumith, “Personalized Job Search with AI: A Recommendation System Integrating Real Time Data and Skill Based Matching,” International Journal of Engineering Research & Technology, vol. 14, no. 4, Apr. 2025, doi: https://doi.org/10.17577/ijertv14is040176
