IntelCV: An Intelligent Skilled Resume Selection Method for Job Purposes

Abstract

In today’s companies recruit a significant number of employees each year and face the challenge of evaluating a vast quantity of resumes from candidates. Consequently, the HR department finds it challenging to filter the CVs that best match the company's requirements. This thesis addresses this challenge by rigorously comparing the accuracy of an automated job-resume matching system against human-led decisions, specifically focusing on filtering applicants based on their skill sets. The study employs preprocessing techniques to standardize text and advanced Natural Language Processing (NLP) techniques to extract skills from 122 potential resumes across 10 technical job positions. Using Cosine similarity, Jaccard index, and Jaro-Winkler distance, the research analyzes the similarity between job descriptions and resumes. The decisions made by the automated system are then compared with the decision made by human assessors, and their alignment is meticulously evaluated. Furthermore, leveraging K-means clustering, the study categorizes resumes into "good," "average," and "poor" groups, providing recruiters with efficient tools to identify the most qualified candidates. This research aims to shed light on the strengths and limitations of machine-based decision-making in recruitment, offering insights into enhancing efficiency, optimizing time usage, reducing biases, and improving candidate evaluations. Through the evaluation of three similarity measurement algorithms, the study identifies that Cosine similarity and Jaro-Winkler distance provide high accuracy results, while the Jaccard index yields lower results. The achieved research results contribute to a deeper understanding of automated systems' efficacy in the recruitment landscape, leading to the development of more informed and effective talent acquisition strategies, especially when compared to time-consuming and biased manual recruiting processes.