An Intelligent NLP-Based System for Resume Screening and Applicant Tracking Systems (ATS)-Optimised Resume Generation

Amir 'Aatieff  Amir Hussin; Sherina Aleeyah Suparman; Hannah Sufiyyah Rushdan

doi:10.31436/ijpcc.v12i2.719

Authors

Amir 'Aatieff Amir Hussin Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
Sherina Aleeyah Suparman Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
Hannah Sufiyyah Rushdan Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.31436/ijpcc.v12i2.719

Keywords:

Natural Language Processing, Sentence-BERT, Resume Screening, Applicant Tracking Systems, Skill Extraction, Explainable AI

Abstract

This paper presents the design, development, and evaluation of a web-based intelligent Progressive Web Application (PWA), that solves key issues arising from inefficiencies of current recruiting and resume writing processes. Traditionally, Applicant Tracking Systems (ATS) depend on keyword-based searching and matching, often miss out on relevant candidates because of semantic mismatch, and do not give constructive feedback to the job applicants. In this regard, a novel system that features a double-module architecture, comprising a Recruiter Module and a Job Seeker Module, has been designed to fill this void. The system makes use of named entity recognition (using spaCy and regex) for skills extraction and Sentence-BERT (all-MiniLM-L6-v2) embedding, cosine similarity for deep semantic resume-to-job matching. As a contingency plan, TF-IDF is used to ensure the system's reliability. Evaluation results show a 100% user acceptance testing (UAT) functional pass rate in 20 tests, 100% semantic matching accuracy for high, partial, and low match cases, and 97.5% Precision, 87.7% Recall, and 92.2% F1-score for NLP pipeline skills extraction.

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