Enhanced Resume Screening for Smart Hiring Using Sentence-Bidirectional Encoder Representations from Transformers (S-BERT)

In a world inundated with resumes, the hiring process is often challenging, particularly for large organizations. HR professionals face the daunting task of manually sifting through numerous applications. This paper presents ‘Enhanced Resume Screening for Smart Hiring using Sentence-Bidirectional Encoder Representations from Transformers (S-BERT)’ to revolutionize this process. For HR professionals dealing with overwhelming numbers of resumes, the manual screening process is time consuming and error-prone. To address this, here the proposed solution is developed for an automated solution leveraging NLP techniques and a cosine distance matrix. Our approach involves pre-processing, embed- ding generation using S-BERT, cosine similarity calculation, and ranking based on scores. In our evaluation on a dataset of 223 resumes, our automated screening mechanism demonstrated remarkable efficiency with a screening speed of 0.233 seconds per resume. The system’s accuracy was 90%, showcasing its ability to effectively identify relevant resumes. This work presents a powerful tool for HR professionals, significantly reducing the manual workload and enhancing the accuracy of identifying suitable candidates. The societal impact lies in streamlining hiring processes, making them more efficient and accessible, ultimately contributing to a more productive and equitable job market.

@article{Deshmukh2024,
  title     = {Enhanced Resume Screening for Smart Hiring Using Sentence-Bidirectional Encoder Representations from Transformers (S-BERT)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Asmita Deshmukh and Anjali Raut},
  doi       = {10.14569/IJACSA.2024.0150825},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150825}
}