Title: Design of hybrid SVM job recommender system for the overlapping target classes

Authors: R. Santhosh Kumar; N. Prakash

Addresses: Department of Information Technology, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamilnadu, India ' Department of Information Technology, B.S. Abdur Rahman Crescent Institute of Science and Technology, Vandalur, Chennai, Tamilnadu, India

Abstract: The fresh graduates with no prior experience are struggling to find suitable jobs. The job searching time of the fresh graduate is not reduced. Few researchers used machine learning models for matching the recommended job skill-set with the graduate skill set. If the skill-set of the two jobs is the same, the machine learning algorithms recommend only one job and ignore the other job. To address this problem, we design a hybrid support vector machine job recommendation (HSJR) model. The proposed HSJR model collects the skill set of the graduate and matches it with the current jobs and recommends the most suitable jobs for the graduates. To evaluate the proposed HSJR model, the jobs are recommended for the engineering graduates and the feedback received from the participants. The proposed HSJR model achieves 90% accuracy in the job recommendation. The proposed HSJR model performs better than the traditional job recommender system.

Keywords: job recommender system; support vector machine; SVM; skill set; career recommendation; recommendation system; recommender system.

DOI: 10.1504/IJIDS.2025.149305

International Journal of Information and Decision Sciences, 2025 Vol.17 No.3, pp.243 - 260

Accepted: 15 Feb 2024
Published online: 24 Oct 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article