Optimization of Enterprise Recruitment Strategy Using Lightgbm Decision Tree: Improving Talent Matching Accuracy

The traditional recruitment strategy of enterprises mainly relies on experience judgment and simple data analysis, and there are obvious shortcomings in the accuracy and efficiency of talent matching. To improve the accuracy of talent matching, this article designs a recruitment strategy optimization method based on the LightGBM (Light Gradient Boosting Machine) decision tree algorithm. This method can predict the matching degree between candidates and positions more accurately through refined analysis of candidate resume data, interview performance, and career needs. At the same time, LightGBM's hierarchical decision-making mechanism and gradually optimized branch structure enable it to more finely distinguish subtle differences in candidate features, improving the accuracy of matching prediction. The experimental results show that the LightGBM model performs robustly in management recruitment, with an accuracy rate of 78.6%, precision of 75.4%, recall rate of 80.1%, and F1 score of 77.7%. The recruitment strategy optimization method based on LightGBM decision tree in this article significantly improves the talent matching efficiency of enterprises, providing important theoretical basis and practical reference for optimizing human resource management.