The Application of the Depth Model of Precise Matching between People and Posts Based on Ability Perception in Human Resource Management

Abstract

To revitalize talents and give full play to the maximum utility of HR (human resources), it is not enough to accumulate talents alone. HR must be effectively allocated to realize the matching of people and posts. Competency is a personal characteristic of an organization that distinguishes its performance level in a specific job and organizational environment. In order to solve the problem that job seekers’ job-seeking ability is difficult to match the job requirements, this paper combines neural network with traditional HRM (human resource management) algorithm based on ability perception and designs a depth model of accurate matching of people and posts in HR field, which can improve the quality of data training of traditional algorithm. The results show that compared with other algorithms, the $F1$ value of the proposed algorithm is obviously improved, and the proposed algorithm performs best, with the $F1$ value of 0.829. In this paper, the method of global network plus local network is used, which can effectively improve the hidden features of data and then improve the matching degree and recommendation accuracy of the algorithm.