


Volume 20 No 20 (2022)
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A USER PROFILE BASED RECOMMENDER SYSTEM FOR PRIVATE JOB MATCHING
Gajjela Monika, M. Sridevi
Abstract
A job classification system is used to group positions that have similar duties and
responsibilities. If done correctly, it creates parity in job titles and consistency in job
levels within the organization hierarchy. Job classification is based upon various aspects
of the job and does not take into consideration the person assigned. Instead, identified
job value factors such as technologies, skills, qualifications, etc. are taken into
consideration. These job value factors allow an organization to compare jobs which
may not appear to be similar. Job value factors work because almost every job has
them. From the dataset built by studying various LinkedIn profiles of the users,
proposed methodology want to predict someone’s job category based on his job
summary using classification algorithms. Job summaries are created by users to
describe their skills and tasks. Our goal is to use extract information from these freeform text fields and predict the occupation of the user. For this used Naïve Bayes and
Support Vector Machine (SVM) algorithm. In this project, developed prototype
application is described the working procedure of Job Prediction and shown
performance results and prediction results using Python Django framework.
Keywords
Naive Bayes, SVM, Job Prediction Performance
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