Human Resource Recommendation Based on Decision Tree Algorithm

In view of the limitations of the traditional minimum spanning tree algorithm in human resource recommendation research, a resource recommendation research scheme based on decision tree algorithm was proposed. Firstly, the influencing factors is accurately located through the probability information theory, and the indicators is reasonably divided to reduce interference, and the decision tree algorithm is used to construct a resource recommendation research scheme. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional minimum spanning tree algorithm in terms of the accuracy of resource recommendation research and the processing time of influencing factors, and has obvious advantages. Resource recommendation research plays an extremely important role in human resources, which can accurately predict and optimize the growth characteristics and product generation of human resources. However, the traditional minimum spanning tree algorithm has certain limitations in solving resource management simulation problems, especially when dealing with complex problems. In this paper, this paper proposes a resource recommendation research scheme based on decision tree algorithm to better solve this problem. In this scheme, the influencing factors is accurately located through the probability information theory, so as to determine the division of indicators, and the decision tree algorithm is used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on decision tree algorithm can better solve the limitations of the traditional minimum spanning tree algorithm and improve the simulation accuracy and efficiency in the research of human resource recommendation.