Deep Learning and Optimization Approaches in an Effective Progressive Dense Self-Attention Based Human Resource Recommendation for Predicting Employee Turnover: A Review

Abstract

Employee turnover prediction has emerged as a critical challenge in modern human resource analytics, driven by the need to retain talent and reduce organizational costs. Recent advancements in deep learning and optimization techniques have significantly enhanced predictive capabilities by capturing complex patterns in workforce data. This review explores the integration of progressive dense neural architectures with self-attention mechanisms for improving employee turnover prediction and recommendation systems. The proposed paradigm leverages dense connectivity to enhance feature reuse and gradient flow, while self-attention enables the model to capture long-range dependencies and contextual relationships within employee data. Additionally, optimization strategies such as evolutionary algorithms and metaheuristic techniques contribute to improved model convergence and decision-making efficiency. The paper systematically analyzes existing studies, highlighting key methodologies, datasets, and performance metrics used in turnover prediction. Furthermore, it discusses the role of recommendation systems in providing actionable insights for employee retention and workforce planning. The review identifies current research gaps, including data imbalance, interpretability challenges, and scalability issues. By synthesizing state-of-the-art approaches, this paper provides a comprehensive understanding of intelligent HR analytics frameworks and outlines future research directions for developing robust, adaptive, and explainable turnover prediction systems.x