Deep Learning Based Recommendation System for Employee Retention Using Bipartite Link Prediction - Siregar, Ivan Michael; Othman, Zulaiha Ali; Bakar, Azuraliza Abu (Universitas Serang Raya, 2025)
The Human Resources (HR) department faces significant challenges in employee retention. Traditional methods, such as performance evaluations and career development using regression, association, and clustering, have been widely used and have yielded positive results. However, these approaches are limited in predicting changes in employee behaviour and capturing complex relationships between variables. In this study, we leverage AI advancements to enhance predictive analysis by utilizing deep learning’s ability to identify patterns and complex relationships while continuously adapting to employee behavior changes. Specifically, we integrate Graph Convolutional Network (GCN) deep learning-based and bipartite graph-based approaches to construct a robust link prediction model. The bipartite employee-training network serves as input to the GCN, where each convolutional layer aggregates information from neighboring nodes, leveraging observed link information at each hidden layer. During the evaluation phase, the model iteratively aggregates information until an optimal state is reached, uncovering hidden relationship patterns that facilitate employee skill development. Empirical results on a benchmark dataset demonstrate significant performance improvements, with precision, recall, and AUC metrics exceeding 80%, highlighting the model's effectiveness in enhancing employee retention.