Intelligent Resume Matching System

Abstract

Optimally efficient and smart resume screening is a key factor towards optimizing the recruitment processes, particularly with the recent huge number of applicant resumes in modern day labor markets. The contribution put forward herein suggests an Intelligent Resume Matching System to efficiently automate shortlisting resumes against vacancy announcements based on powerful natural language processing (NLP) methodologies and machine learning approaches The system makes use of BERT-based sentence transformers to create high-dimensional contextual embeddings from job postings and resumes both, directly derived from PDF documents through pdfplumber. To measure candidate-job fit numerically, the system calculates cosine similarity between embeddings and enables dynamic threshold-based filtering. Candidates whose resumes are at or above similarity threshold are shortlisted and informed through automated emails. Additionally, the system has a feedback mechanism powered by a large language model (LLM) to suggest personalized messages for unselected candidates, providing constructive feedback on possible skills or experience gaps. The proposed system not only increases the accuracy of resume-job matching but also minimizes manual labor and guarantees customized candidate interaction. Through the integration of NLP-based embedding methods with automation and interpretable feedback, the system proves to have potential for large-scale deployment in contemporary hiring pipelines.

Introduction

The hiring process is evolving from manual to intelligent, automated systems to improve speed, accuracy, and fairness. A major challenge is efficiently screening large volumes of unstructured resumes, where manual or keyword-based methods fall short due to inefficiency and poor contextual understanding.

To address this, the project proposes an Intelligent Resume Matching System (IRMS) that uses Natural Language Processing (NLP) and Machine Learning (ML) techniques, specifically BERT-based sentence transformers, to semantically parse and compare resumes and job descriptions via embeddings and cosine similarity. This system automates resume parsing, ranking, and candidate shortlisting with configurable similarity thresholds.

A key innovation is its candidate-centered communication: shortlisted candidates receive acceptance emails, while those not selected get personalized, constructive feedback generated by a Large Language Model (LLM), enhancing transparency and candidate development.

The system integrates AI-powered resume ranking and tracking and leverages recent advances such as Google Gemini Pro to improve hiring pipelines. Prior research confirms the efficacy of ML-based resume ranking and recommendation systems, as well as real-time feedback tools for dynamic market needs.

The methodology involves:

• Collecting diverse resumes and job postings.

• Cleaning and structuring data using NLP tools.

• Generating semantic embeddings for comparison.

• Computing similarity scores for ranking and shortlisting.

• Using an LLM for generating personalized candidate feedback.

• Deploying the system with Python, enabling batch processing and recruiter dashboards.

Results from a test with three anonymized resumes showed all were shortlisted above a 50% similarity threshold, validating the system’s ranking ability. While the feedback feature was not triggered in this test, it is important for candidate engagement in larger rollouts.

Future improvements include adaptive thresholds, weighting skills by importance, integrating recruiter feedback, and refining the system to better align with human hiring decisions, aiming to create a scalable, transparent, and equitable recruitment platform.

Conclusion

This research ventured into designing and developing an Intelligent Resume Matching System (IRMS), utilizing sophisticated natural language processing (NLP) tools and machine learning methods to facilitate efficient shortlisting of resumes. The system commences with obtaining word embeddings of both job postings and resumes via the most innovative technologies, such as pdfplumber to extract PDF content and BERT Sentence Transformer for obtaining semantic embeddings. By comparing the similarity of the embeddings, the system identifies the resumes that best fit the job requirements. The system uses a user-configurable threshold so that there is room for adjusting the degree of similarity required to tag resumes as shortlisted, thereby allowing the system to be adjusted to varying hiring preferences and requirements. It is also easier to achieve through automated reminder emails. Instant notifications are provided to shortlisted applicants, making it possible for communication and response to occur much faster. The system provides non-shortlisted applicants with individual feedback emails based on Large Language Models (LLMs). These emails briefly state the possible incompatibilities between the skills of the applicant and requirements of the job and offerAn Automated Resume Screening System Using Natural Language Processing and Similarity. constructive criticism to use on future applications. The feedback not only enhances the candidate experience but also aids in the professional growth of the candidates by highlighting areas of improvement. In total, the IRMS is a gem of innovation in recruitment, promising efficiency and transparency. Through resume shortlisting and feedback, automated by the system, the employer\'s recruitment process is maximized while the candidate experience is enhanced, giving a better and better-informed job application process. The Intelligent Resume Matching System can have immense potential for future development in functionality, inclusiveness, and user-friendliness. The system can be augmented with the following functionalities to significantly enhance its capabilities: A. Multilingual Capability To facilitate a global talent pool, future expansion may involve multilingual NLP models that can understand and process resumes and job descriptions across several regional and global languages.This enables recruiters to tap into diverse talent and allows for unbiased hiring that transcends geographic boundaries. Utilizing pretrained models such as mBERT or XLM-Roberta would allow semantic matching outside of English, thus eliminating language barriers in talent attraction. B. Bias Detection and Mitigation As recruitment processes increasingly depend on equity and fairness, the mechanism can be augmented with bias detection features to verify and prevent potential gender, ethnicity, or age bias. Algorithms applied to NLP with a sense of justice will validate resume matching solely on merit. Ongoing auditing and use of de-biasing protocols during training and embedding generation will validate ethics in the hiring process. C. Voice Input for Job Descriptions For the sake of enhancing usability and reducing manual work of entry, the site can include voice-to-text functionality to allow recruiters to describe job requirements verbally. Such functionality would use speech-to-text APIs (i.e., Google Speech-to-Text or Whisper) to convert spoken words into organized text, thus improving the usability of the platform, especially for visually impaired or non-technical users. D. Interactive Visual Dashboard for Decision Support An intuitive, real-time dashboard can be brought in to visually display matching scores, candidate rankings, gap analysis, and filtering metrics. It would enable recruiters to easily interpret and contrast levels of candidate fit through graphs, heatmaps, and skill-match visualizations. A dynamic UI increases transparency in the decision process and enables recruiters with actionable insights throughout talent assessment.

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Copyright

Copyright © 2025 Dr. Y Sagar, K S G Prakeerth, G Abhiram, N Mahideep, M Poojith. 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.