Research on resume screening methods in corporate internet recruitment based on machine learning

With the development of information technology, most domestic companies mainly use Internet recruitment methods for talent recruitment. Various Internet platforms have a large number of talent resumes. How to quickly and accurately discover and recruit talents that meet the company's development needs has become the focus of many companies and scholars. As the number of job-seeking users continues to rise, the number of resume resources increases dramatically, and a large number of resumes are misclassified. Therefore, in the massive dynamic resume resources, correct classification faces great challenges and is an urgent problem to be solved. Aiming at the low efficiency of manual resume screening in human resources recruitment, this article studies building a job talent model before screening resumes, obtaining key resume data on the Internet platform, and combining it with machine learning algorithms to provide a solution for automatic resume screening. This article proposes a dynamic resume resource classification and screening method based on machine learning, and a resume screening method that is dynamically adjusted based on changes in corporate talent requirements. Experimental results show that this method can effectively reduce the company's human recruitment costs and improve the efficiency of talent recruitment.