Smart Technology and Artificial Intelligence
AI-Driven Resume Quality Evaluation Using Semantic Feature Analysis and Ensemble Learning
Authors: Sapna Bansal, Rashi Mittal, Priya Saini, Anshuka Sahni, Diya Tiwari and Bhupinder Kaur Srao
Publishing Date: 04-06-2026
ISBN: 978-81-975670-2-5
Abstract
In modern recruitment environments, organizations receive a large volume of resumes, making manual screening inefficient and prone to bias. This paper proposes an AI-driven resume quality evaluation framework that automates candidate assessment using natural language processing and machine learning techniques. The proposed system extracts structured information from resumes and job descriptions and evaluates candidates using semantic similarity and feature-based scoring. A novel Multi-Layer Temporal Skill–Experience Graph Alignment (MTSEGA+) approach is introduced to model candidate profiles using temporal graph structures, capturing skill evolution and career progression. The framework integrates ensemble learning with graph-based alignment to improve prediction accuracy and robustness. Experimental evaluation was conducted on a dataset of approximately XXXX resumes, demonstrating that the proposed model outperforms traditional machine learning approaches such as Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. The results show improved accuracy (0.93), precision (0.92), recall (0.91), and F1-score (0.92). The proposed system enables scalable, consistent, and interpretable candidate shortlisting, contributing to intelligent recruitment systems.
Keywords
Natural Language Processing (NLP), Resume Screening, Candidate Selection, Semantic Similarity, Recruitment Automation.
Cite as
Sapna Bansal, Rashi Mittal, Priya Saini, Anshuka Sahni, Diya Tiwari and Bhupinder Kaur Srao, "AI-Driven Resume Quality Evaluation Using Semantic Feature Analysis and Ensemble Learning", In: Mukesh Saraswat, Sandeep Kumar, Manjunatha Sughaturu Krishnappa and Rakesh Keshava (eds), Smart Technology and Artificial Intelligence, SCRS, India, 2026, pp. 33-52. https://doi.org/10.56155/978-81-975670-2-5-4