Job Recommendation System Using Deep Reinforcement Learning (DRL)

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10s.7701
Keywords:
Recommended systems, Deep Learning, Reinforcement Learning, Optimization

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Srinivasa Rao Mandalapu
Research Scholar, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu
B. Narayanan
Assistant Professor, Department of Computer Science and Engineering, Annamalai University Chidambaram, Tamilnadu
Sudhakar Putheti
Professor, Department of CSE, Vasireddy Venkatadri Institute of Technology, Namburu, Pedakakani, Guntur, Andhra Pradesh, India

Abstract

The rapid growth of online job portals and the increasing volume of job listings have made it challenging for job seekers to efficiently navigate through the vast number of available opportunities. Job recommendation systems play a crucial role in assisting users in finding relevant job opportunities based on their skills, preferences, and past experiences. This research paper proposes a job recommendation system that leverages deep learning techniques to enhance the accuracy and effectiveness of job recommendations. The system utilizes advanced algorithms to analyses user profiles, job descriptions, and historical data to generate personalized job recommendations. Experimental evaluations demonstrate the superiority of the proposed system compared to traditional recommendation methods, thereby improving the job search process for both job seekers and employers. This paper provides Job recommendation system using Deep Reinforcement Learning (DRL).

Article Details

How to Cite
Mandalapu, S. R. ., Narayanan, B. ., & Putheti, S. . (2023). Job Recommendation System Using Deep Reinforcement Learning (DRL). International Journal on Recent and Innovation Trends in Computing and Communication, 11(10s), 621–630. https://doi.org/10.17762/ijritcc.v11i10s.7701
Issue
Vol. 11 No. 10s (2023)
Section
Articles

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