NLP-Based Resume Screening and Job Recruitment Portal

Kadam, Rohit S.; Suhas, Gloria; Mukri, Uroosa; Khandare, Suraj

doi:10.1007/978-981-16-6460-1_1

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Manual assessment or screening is somewhat biased because it relies heavily on how a person perceives or interprets the resume, whereas an automated resume screening system with a machine learning approach will remove the bias in screening the resumes. NLP (Natural Language Processing) can be utilized to match job applicants with job openings based on a keyword [12].…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning-Based Recommendations and Classification System for Unstructured Resume Documents

Channabasamma¹,

Suresh²

2023

RIA

View full text Add to dashboard Cite

With the burgeoning growth of the job market and a surge in applications, the processes of job recommendation and candidate selection have become complex and labor-intensive. The advent of new technologies such as machine learning has automated these processes, yet the unstructured nature of resumes, often in PDF format, necessitates laborious data extraction for efficient skill-based candidate screening and categorization. Ineffectual recruitment can result from mismatched skills. The system proposed in this study aims to address these challenges by automatically fetching and categorizing resumes, extracting critical information, and utilizing job descriptions for candidate selection and recommendations. Unstructured data from PDF documents is extracted using a PDF reader, and machine learning algorithms, specifically logistic regression and Gaussian Naï ve Bayes, are employed for generating recommendations. In an innovative approach, this system not only classifies resumes but also recommends updates or rewrites. Performance of the proposed system is evaluated in terms of classification accuracy and the effectiveness of update recommendations, and results are compared with alternative models. This research represents a significant advancement in the application of machine learning to the automation of job recommendation and candidate selection processes.

show abstract

Section: Literature Surveymentioning

confidence: 99%