AI Based Job Recommendation System using Skills Analysis

Abstract

Abstract—Recruitment and job matching are very
important processes which affect both the recruitment
process and the job. Productivity and career
development of organizations and individuals.[1],[14]
The old-fashioned recruitment methods focus on
manually checking resumes, keywording searches, and
subjective assessments. Methods that lead to inefficient
candidate selection, extended hiring process and
inaccurate alignment of jobs with candidates. In order
to overcome these limitations, the present paper
introduces an AI-based Job Recommendation and
Candidate Matching System which automatically
recommends jobs to the job seekers based on their CVs
and automatically matches them with job applicants,
which are generated by the system. Uses Natural
Language Processing (NLP) and intelligent similaritybased ranking to improve the recruitment process.
[1],[7],[12]efficiency of job seekers and employers alike.
The suggested system will be a two-platform system
with special modules for students/job seekers and for
parents/guardians, employers. Job seekers can develop
comprehensive profiles with information about their
qualifications, technical skills, certifications and project
experience, and Employers can publish the
requirements for a job and browse for candidates. It
uses NLP preprocessing techniques such as text
normalization, tokenization, removal of stop words and
skills. To extract, to convert profile and job description
data from an unstructured to a meaningful
representation. The system uses the TF- IDF inspired
term weighting and weighted feature vectors to enhance
the quality of the recommendations. Highlight the need
for critical skills, certifications and qualifications.
Candidate profiles and job descriptions are vectorized
and cosine similarity is measured to determine the
similarity between candidates and job descriptions. A
semantic connection between their meaning.
Furthermore, there is an exact skill matching boost
system to incentivize skill matching at the key skill level,
and This is followed by calculating a hybrid score based
on both semantic similarity and skill overlap to give a
recommendation score.[6],[7],[15] This method allows to
rank jobs better for candidates and candidates better
for the employers.
The system generates personalized job recommendations,
identifies matched and missing skills, and provides
transparent AI-based match scores to improve user trust
and decision-making. Employers save the time of manual
screening, as well as enjoy automated candidate ranking
and shortlisting. recruitment time. The proposed solution
is not only able to identify meaningful relationships
among skills but also is different from traditional
keyword-based approaches. It improves the accuracy of
profile matching and better hiring outcomes due to
profile attributes. The use of NLP pre-processing, TF-IDF
weighting, cosine similarity, and cosine similarity
clustering proves to be effective in experimental
evaluation. hybrid scoring improves the accuracy of
recommendations, while providing scalable support for
today's recruitment environments. The proposed system
helps in the efficient alignment of candidate-job, which in
turn helps in improving the process of intelligent talent
acquisition. Recruitment quality, and helping to make
data-driven hiring decisions.