Job Recommendation System

Online recruitment platforms have led to a surge in job applications, creating challenges for companies in managing and reviewing them ‎efficiently. With the exponential growth of online job postings and user profiles, the challenge of matching the right job to the right candidate has become increasingly complex. Job recommendation systems aim to streamline this process by leveraging advanced algorithms to ‎suggest relevant job opportunities to job seekers based on their skills, experience, and preferences. Our proposed technique performs better ‎in terms of personalized job recommendations to different users based on their profiles‎.

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Publication Details

Journal
International Journal of Basic and Applied Sciences
Published
2025-07-20
DOI
https://doi.org/10.14419/mhjxnq18
Primary Topic
Customer churn and segmentation
Type
article
Field-Weighted Citation Impact
0.00
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article

Job Recommendation System

Girish Deshmukh, Mona Deshmukh, Ameya Parkar
International Journal of Basic and Applied Sciences
Customer churn and segmentation
article

Job Recommendation System

Girish Deshmukh, Mona Deshmukh, Ameya Parkar
article en

Abstract

Online recruitment platforms have led to a surge in job applications, creating challenges for companies in managing and reviewing them ‎efficiently. With the exponential growth of online job postings and user profiles, the challenge of matching the right job to the right candidate has become increasingly complex. Job recommendation systems aim to streamline this process by leveraging advanced algorithms to ‎suggest relevant job opportunities to job seekers based on their skills, experience, and preferences. Our proposed technique performs better ‎in terms of personalized job recommendations to different users based on their profiles‎.

International Journal of Basic and Applied SciencesVol. 14(3)
University of Mumbai (IN), D.Y. Patil University (IN)
Openalex Percentile: Top 77%
Customer churn and segmentation
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