Recent Advances in an Effective Progressive Dense Self-Attention based Human Resource Recommendation for Predicting Employee Turnover: A Systematic Review

Abstract

Employee turnover prediction has emerged as a critical challenge in modern organizational management due to its direct impact on productivity, cost, and workforce stability. Recent advancements in artificial intelligence, particularly deep learning, have enabled the development of sophisticated predictive models that capture complex employee behavior patterns. This paper presents a systematic review of progressive dense self-attention-based human resource recommendation systems designed for predicting employee turnover. The study explores the integration of dense neural architectures with self-attention mechanisms to enhance feature representation, contextual understanding, and temporal dependencies in HR datasets. By analyzing recent literature, the review identifies key methodologies, datasets, evaluation metrics, and performance trends in this domain. The findings indicate that progressive dense architectures improve information flow and mitigate vanishing gradient issues, while self-attention mechanisms enable the model to focus on critical employee attributes influencing turnover. Additionally, hybrid models combining deep learning with optimization techniques demonstrate superior predictive accuracy and interpretability. This review highlights current challenges such as data imbalance, privacy concerns, and model generalization, while also outlining future research directions for building robust and scalable HR recommendation systems. The study serves as a comprehensive resource for researchers and practitioners aiming to leverage advanced AI techniques for workforce analytics and decision-making.