Hierarchical dilated parrot causal convolutional networks for optimizing human resource recommendations

An essential role in any firm is human resource management, or HRM, with effective matching of employees to suitable positions remaining the main employment issue. Traditional methods often rely on simplistic data models, which fail to fully capture the complex relationships between employee attributes, job requirements, and performance outcomes. To overcome these issues, this manuscript is proposed. This manuscript presents a novel approach, Hierarchical Dilated Parrot Causal Convolutional Attention Networks (HieDil-P2CAN), designed to enhance human resource recommendations. The input dataset, titled “human resource recommendations,” contains synthetic employee data generated for comprehensive HR analysis. Preprocessing is performed using the Fuzzy Min–Max Neural Network. The technique creates regular presentations of data that are easy to understand. Feature extraction is achieved through the Q-value Regularized Transformer, which captures the critical attributes influencing job matching. The prediction phase employs the HieDil-P2CAN, integrating Dilated Causal Convolution Networks with Hierarchical Attention Models and optimized using the Parrot Optimizer to effectively predict and recommend the best human resource solutions. The framework demonstrates an impressive 99.4% accuracy, optimizing human resource recommendations by accurately matching employees to suitable jobs based on their skills and performance metrics. This results in improved employee satisfaction and organizational efficiency. The system also significantly reduces computational time and enhances the precision of HR decision-making processes.