A hybrid job recommendation system based on resume and applicant records using K-EM and SVM algorithms

Given the increasing number of jobs and job seekers, it seems necessary to have a system that can provide suitable jobs to job seekers on the web. The best way to do this is to use recommender systems. Recommender systems can provide users with suggestions that are of interest to them. In this study, we present a new method to improve recommender systems in the field of job suggestions to users. The working method is that we first collect the records of individuals' jobs, then classify this data using data mining algorithms and according to each person's interest. Then, we will provide this data to the user using a recommender system based on collaborative filtering. The results of the evaluation of the proposed method indicate the high performance of this proposed system. In such a way that the proposed method was able to achieve an accuracy rate of 92% and a recall rate of 96%, and in general, this system can provide up to 90% of recommendations to users that can be of interest to the target user to a high percentage.