Survey on Job Recommendation Systems using Machine Learning

Abstract

With the development of internet technology online job hunting has been boosted as it helps to save the time and efforts, [theory providing ease of search]. It's hard for job seekers to rely solely on keyword acquisition to find a job that befit their needs. To overcome this problem, the system will be made using article-based collaborative filtering and content-based filtering job recommended algorithm. The proposed system will be notified where the information about various jobs could be scrapped from vivid websites to form a huge database comprising majority of information regarding real time job opportunities. The users' information would be fetched from the CV submitted with our data from the dataset and recommendations would be provided on the same. This would prove to be of greater help as one doesn't have to hunt across varied websites. Job recommender systems' goal is to offer suggestions based on data about the users' preferences that has been recorded. The major goal is to provide skill recommendations to users so they can learn them, discover appropriate work, and streamline the application process for both novice and seasoned job seekers. The difficulties lie in identifying the best individuals based on their skill sets.