Personalized Employment Information Recommendation Based on Data Mining and Decision Tree-Random Forest Algorithm

The current employment system has many problems in talent selection and matching, such as the mismatch of people and posts caused by information asymmetry, the difficulty of accurately considering the characteristics of complex talents by traditional screening methods and the lack of a dynamic adjustment mechanism to adapt to the rapidly changing employment market. In order to improve the accuracy and preference of employment information recommendation results, a data mining model is introduced to design and study the personalized recommendation method of employment information. First, the system development and design of the data mining employment system, the recommendation framework is described and the features are extracted. Second, the employment information and features are clustered, and the Apriori algorithm is used to analyze the association of employment information. Finally, according to the employment needs of different job seekers, personalized intelligent recommendation is provided. The experimental results show that the paper uses the data mining algorithm-decision tree-random forest (DT-RF) and other algorithms (SVM, KNN and AdaBoost) to compare the performance on different platforms. Under the Hadoop platform, it can have a higher processing time. In terms of recommendation effect, the recommendation model using the DT-RF algorithm has better performance in recommendation accuracy and RMSE. The combination method effectively improves the accuracy and efficiency of talent recommendation, significantly improves the problem of information asymmetry, can flexibly adjust the recommendation strategy according to the market dynamics, provides a practical way to solve the problems existing in the current employment system, and helps to promote the efficient operation of the employment market and the rational allocation of talents.