Support vector machine (SVM) algorithm optimization and innovative talent matching model for employment needs

Abstract

Traditional talent matching methods generally rely on manual rules and static feature analysis, which makes it difficult for the model to adapt to the rapidly changing employment market and the personalized needs of job seekers, resulting in insufficient matching precision and poor adaptability. This paper constructs an innovative talent matching model based on the optimized support vector machine (SVM) algorithm to address this problem. Firstly, dynamic employment market data and multi-dimensional job seeker features are used to build a more intelligent and personalized matching framework. This study proposes an innovative intelligent talent matching model that enhances the understanding of the relationship between jobs and job seekers through data cleaning, standardization, and feature extraction using TF-IDF technology. By optimizing the SVM kernel function and fine-tuning hyperparameters, the model’s classification performance in complex matching tasks is improved. Additionally, the integration of real-time dynamic data updates and incremental learning methods enables the model to automatically adapt to market changes, improving the timeliness and accuracy of matching results. In the design of the multi-dimensional matching model, this paper further integrates job seeker potential analysis and job development potential to optimize the recommendation strategy. Compared to traditional keyword matching and logistic regression models, the proposed model significantly outperforms others in talent matching, achieving a maximum matching accuracy of 0.91, a maximum F1-score of 0.93, an average response time of 2.02 minutes, and an average update frequency of 14.03 times per hour. The results demonstrate that this innovative talent matching model provides a more efficient, personalized, and intelligent solution for the dynamic employment market, advancing the development of talent matching technology.