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

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.