Resume Clustering and Job Description Matching

Abstract

The efficient functioning of drainage systems is critical for urban infrastructure, playing a key role in managing stormwater, preventing flooding, and safeguarding public health and safety. ABSTRACT: In today’s competitive job market, both job seekers and employers are increasingly turning to automated systems to improve the efficiency of the hiring process. This paper explores Resume Clustering and Job Description Matching, two key aspects of recruitment technology, which are designed to facilitate faster, more accurate hiring decisions. We propose a methodology for automatically matching resumes to job descriptions by leveraging machine learning algorithms, natural language processing (NLP) techniques, and deep learning methods. Resume clustering groups similar resumes to enable HR professionals to better understand the candidate pool, while job description matching ensures that the resumes align with the specific requirements of job postings. Our proposed system integrates both techniques, aiming to reduce manual effort and human bias, ultimately enhancing recruitment efficiency. By utilizing these technologies, organizations can filter and rank candidates based on the relevance of their qualifications to job descriptions, ensuring that they select the most qualified candidates with greater speed and accuracy.[1]

Introduction

The recruitment process is increasingly automated using machine learning (ML), natural language processing (NLP), and artificial intelligence (AI) to handle the growing volume of job applications efficiently and fairly. Traditional manual resume screening is time-consuming and prone to bias, so automating resume clustering and job description matching helps identify the best candidates more quickly and objectively.

Resume clustering groups similar resumes based on features like skills, experience, and education, enabling HR to focus on relevant candidate groups. Job description matching compares resumes with job requirements using advanced semantic techniques beyond keyword matching, improving accuracy by understanding context and synonyms.

The literature shows progression from basic keyword methods to sophisticated models using word embeddings and transformer-based deep learning (e.g., BERT), enhancing matching accuracy. Clustering algorithms like K-means help organize large datasets, and combined approaches improve speed and precision.

The proposed system preprocesses resumes and job descriptions, extracts features (TF-IDF and embeddings), clusters resumes, then matches job descriptions against relevant clusters using similarity measures like cosine and Jaccard similarity. It ranks candidates by match scores and presents results through a user-friendly interface.

An automatic screening feature reduces manual workload, while a feedback loop allows HR professionals to refine rankings, enabling the system to learn and improve over time. This integrated approach aims to streamline recruitment, reduce bias, and improve hiring decisions at scale.

Conclusion

This research proposes an integrated approach for Resume Clustering and Job Description Matching using advanced machine learning and NLP techniques. By combining unsupervised learning for clustering with semantic matching algorithms, our system provides an efficient, scalable, and accurate solution for automating the recruitment process. The expected outcomes demonstrate the potential for reducing hiring time, improving candidate-job fit, and supporting HR professionals in making more informed decisions. Future work could focus on the use of deep learning models like BERT to further improve the system’s matching capabilities and enhance its adaptability to various industries.[10]

References

[1] Purohit, A., Sharma, V., & Patel, R. (2018). Enhancing resume-job matching using semantic similarity and contextual embeddings. International Journal of Computer Applications, 180(43), 1–6. [2] Kaur, J., & Kumar, A. (2020). Resume classification and recommendation using machine learning techniques. International Journal of Advanced Computer Science and Applications, 11(5), 123–129. https://doi.org/10.14569/IJACSA.2020.0110516 [3] Chowdhury, S., Alam, M., & Saha, S. (2019). Unsupervised clustering techniques for resume classification in recruitment systems. In Proceedings of the International Conference on Data Mining and Big Data Analytics (pp. 142–153). [4] Li, Y., Zhang, H., & Wang, J. (2021). Semantic matching of resumes and job descriptions using BERT-based deep learning models. Journal of Artificial Intelligence Research and Development, 38(2), 215–229. [5] Dhingra, A., Singh, R., & Malhotra, S. (2020). An end-to-end intelligent recruitment system using deep learning and natural language processing. International Journal of Information Management Data Insights, 1(2), 100019. https://doi.org/10.1016/j.jjimei.2020.100019 [6] Zhou, X., & Li, M. (2022). Multimodal resume-job matching using textual and behavioral data fusion with BERT embeddings. Expert Systems with Applications, 195, 116582. https://doi.org/10.1016/j.eswa.2022.116582 [7] Sharma, P., & Dubey, A. (2017). Keyword-based resume screening: Limitations and alternatives. International Journal of Computer Sciences and Engineering, 5(10), 85–89. [8] Verma, T., & Jain, S. (2016). Semantic similarity techniques for improving resume screening. Procedia Computer Science, 89, 374–381. https://doi.org/10.1016/j.procs.2016.06.076 [9] Gupta, R., & Arora, A. (2018). Machine learning based job matching and candidate ranking system. In Proceedings of the 2018 International Conference on Computational Intelligence and Data Science (pp. 553–558). [10] Gupta, R., Usman, M., Kashid, P. V., Mohan, L., Gaidhani, V. A., & Ghuge, A. R. (n.d.). Artificial Intelligence and IoT in Retail Marketing: Innovations in Smart Stores and Personalized Shopping. Journal of Innovation in Emerging Research, 5(2). https://doi.org/10.52783/jier.v5i2.2473

Copyright

Copyright © 2025 Archana V. Ugale, Sanap Gayatri, Gunjal Rutik, Ghumare Amit, Andhale Shreyas. 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.