International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 01 | Jan 2026
p-ISSN: 2395-0072
www.irjet.net
AI Based Resume Shortlisting and Job Recommendation systems Umesh Kailas Shingare1, Om Balasaheb Taskar2, Kalpesh Subhash Wagh3, Saurav Anil Sultane4, Om vishnu Autade5, Prof D.S.Shingate6 Under the guidance Of Information Technology Met’s Institute Ofengineering,Nashik
-------------------------------------------------------------------------------***-------------------------------------------------------------------------Abstract- In today’s world, companies receive hundreds of resumes for every job opening, making it difficult and timeconsuming for HR teams to find the right candidates. To solve this problem, this project introduces an AI-Based Resume Shortlisting and Job Recommendation System that automates the recruitment process using artificial intelligence and machine learning. This system helps both candidates and HR (Admin) users. Candidates can register, upload their resumes, and instantly receive a resume score, skill improvement suggestions, and job recommendations based on their profile. They can also apply for jobs directly through the system. On the other hand, HR (Admin) can create job posts, view uploaded resumes, and use the machine learning model to automatically find the most suitable candidates for each job. The admin can also provide feedback to candidates who were not selected, helping them understand their skill gaps and improve. Developed using Python and Django, this project makes the recruitment process faster, more accurate, and smarter by combining automation with artificial intelligence to match the right people with the right jobs Key Words: Artificial Intelligence, Machine Learning, Resume Screening, Job Recommendation, Recruitment Automation, Candidate Evaluation.
I. INTRODUCTION In today’s competitive job market, companies receive hundreds of resumes for every job opening. It becomes a difficult and time-consuming task for HR teams to go through each resume manually and find the right candidate. At the same time, job seekers often do not know which jobs best match their skills or how to improve their resumes to stand out. To address these problems, this project introduces an AI- Based Resume Shortlisting and Job Recommendation System developed using Python and Django. The system uses artificial intelligence and ma chine Learning to automatically analyze resumes and match them with the most suitable job roles. There are two types of users in this system: Admin (HR) and Candidate. Candidates can register, upload their resumes, and receive a resume score, skill improvement suggestions, and job recommendations based on their profile. The Admin (HR) can create job posts, view uploaded resumes, and use the AI model to shortlist the best candidates for a particular job. Admins can also provide feedback to candidates who are not selected, helping them identify skill gaps and improve. By using AI to automate resume analysis and job matching, this system makes the recruitment process faster, fairer, and more efficient for both employers and job seekers.
II. BACKGROUND A.AI-Based Resume Shortlisting and Job Recommendation Artificial Intelligence (AI) has reshaped recruitment workflows by automating the processes of resume evaluation, skill extraction, and candidate-job matching. Conventional manual screening is often slow, inconsistent, and susceptible to human bias, especially when organizations receive thousands of resumes for a single opening. AI-based systems address these inefficiencies by applying Natural Language Processing (NLP) and Machine Learning (ML) algorithms to analyze resume content, extract key competencies, and evaluate compatibility with available job descriptions. These intelligent systems transform unstructured text (PDF or DOCX resumes) into structured, comparable data formats. Using textual and semantic similarity measures, they can rank candidates, recommend suitable positions, and even identify missing skill areas. Such systems benefit both candidates, who gain insights into job fit and skill improvement, and HR administrators, who can shortlist top applicants rapidly and objectively. By integrating automated scoring, recommendation engines, and feedback loops, AI-driven shortlisting enhances efficiency, fairness, and data-driven hiring decisions. B. Setbacks in Traditional and AI-Based Systems Despite notable advancements, both conventional and modern AI recruitment approaches present several limitations. Traditional systems rely heavily on keyword matching or manual scanning, which leads to inconsistent outcomes, redundancy, and bias. They often fail to detect context, synonyms, or implied competencies within resumes. For example, a
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