A Job-Seeking Assistance System by Integrating Interview Training With Job Recommendation

University graduates often encounter challenges during their initial job-seeking, such as interview anxiety and insufficient interview experience. To address these issues, this paper designs a job-seeking assistance system that integrates interview training and personalized job recommendation. The system employs Virtual Reality (VR) technology and Large Language Model (LLM) to create immersive interview scenarios for experiential learning. This approach provides a realistic interview simulation environment that facilitates the accumulation of interview experience and helps alleviate interview anxiety. Based on the interview dialogue records, we further propose a novel job recommendation with dual-view-enhanced hybrid expert module to improve recommendation accuracy. Experimental results on a real-world dataset demonstrate the optimal performance of the proposed model. After the interview, the system automatically generates interview analysis reports, including a competency analysis of the user, an evaluation of the target position, and a personalized job recommendation. Although the system is developed and evaluated within the context of the Chinese job market, it offers potential for extension to other cultural and labor market settings.