Dynamic Context‐Weighted Embeddings: A Novel Approach to Predictive Modelling

ABSTRACT Employee satisfaction prediction models often struggle to capture the complex, non‐linear relationships between compensation and job satisfaction, particularly in heterogeneous organisational contexts. This paper introduces a novel deep learning framework incorporating multiple technical innovations to address these challenges. The proposed approach employs a dual‐pathway neural architecture with compensation‐specific processing modules to explicitly model the non‐linear interactions between compensation factors and other job attributes across diverse organisational settings. A differentiated embedding strategy transforms raw features into rich, context‐aware representations, enabling the capture of subtle patterns in employee satisfaction dynamics. The framework integrates a context‐sensitive attention mechanism that automatically identifies and weighs relevant features based on organisational characteristics and temporal patterns, alongside a specialised loss function that adaptively emphasises difficult‐to‐predict cases, improving performance on complex satisfaction patterns. This model demonstrates robust performance across diverse industry settings, handling missing data and class imbalance effectively. Extensive comparative experiments against state‐of‐the‐art methods (LSTM‐Attention, GNN‐based approaches and traditional ML models) across multiple datasets show significant improvements, with prediction accuracy increasing by 5.2%–8.5%, mean squared error decreasing by 10.3%–15.2% and AUC‐ROC metrics improving by 7.8%. Further analysis reveals superior performance in handling temporal dependencies and organisational context variations, with particular strength in predicting satisfaction levels during significant organisational changes.