Automated Resume Screening and Job Recommendation System Using Natural Language Processing and Deep Learning

Vandana Dewangan

Title:
Automated Resume Screening and Job Recommendation System Using Natural Language Processing and Deep Learning

Authors:
Vandana Dewangan

Cite This Article :

Vandana Dewangan "Automated Resume Screening and Job Recommendation System Using Natural Language Processing and Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | Smart Innovations in Computer Science and Applications, March 2026, pp.55-62, URL: https://www.ijtsrd.com/papers/ijtsrd101605.pdf

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Abstract :
Hiring the right candidate is a tough and time-consuming task, especially when HR departments receive hundreds of resumes for a single job post. Going through them manually takes a lot of time and can lead to human bias or errors. To solve this practical problem, we developed "Career Navigator," an automated resume screening system. Unlike older Applicant Tracking Systems (ATS) that only look for exact keywords, our project tries to understand the actual meaning of the text using Natural Language Processing (NLP). We used TF-IDF to extract important features from the resumes and built a Recurrent Neural Network (RNN) to classify them into specific job domains. Additionally, we added a recommendation engine using Cosine Similarity to suggest the best-fitting jobs and show candidates what skills they are missing. Our tests showed promising results, achieving a classification accuracy of 91.4% while heavily reducing the time needed to screen a profile.

Keywords :
Resume Screening, NLP, Deep Learning, RNN, Recommender System, TF-IDF.

Publication Details:

Unique Identification Number : IJTSRD101605

Published In : Special Issue | Smart Innovations in Computer Science and Applications, March 2026

Page Number(s) : 55-62

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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