Enhancing Employee Satisfaction and Retention via Multimodal Deep Learning in Dynamic Human Resources Decision-Making

Effective human resource management (HRM) is essential for optimizing enterprise decisions and enhancing employee satisfaction. However, traditional models rely on single-modal data and fail to adapt dynamically to complex employee behaviors and emotions. To address these limitations, the authors propose HR-GENIE, a deep generative model integrating graph neural networks (GNNs), vision-language models (VLMs), and reinforcement learning (RL). GNNs analyze corporate social networks, VLMs extract emotional insights from multimodal data, and RL optimizes HR policies dynamically. Experiments show that HR-GENIE outperforms baseline models in MSE, RMSE, NDCG@10, employee attrition reduction, and NPS improvement, improving satisfaction prediction, social network analysis, and HR decision-making. Ablation studies confirm the contribution of each component. This study offers a data-driven HRM framework that enables enterprises to develop adaptive and employee-centric strategies, enhancing organizational stability.