Adaptive core-enhanced latent factor model for highly accurate QoS prediction

Accurate prediction of Quality of Service (QoS) plays a crucial role in service recommendation and selection across large-scale distributed environments. Latent factor (LF) models have become a mainstream solution for QoS prediction owing to their simplicity and scalability, yet typical formulations struggle to capture complex latent interactions and usually rely on manually tuned regularization, which often limits prediction accuracy. To address these challenges, we propose an Adaptive Core-Enhanced Latent Factor (ACELF) model that integrates a learnable core interaction mechanism with an incremental Proportional-Integral-Derivative (PID)-driven adaptive regularization strategy. Specifically, a learnable core interaction matrix is introduced to model interactions between latent user and service factors, enabling richer representation learning beyond standard bilinear assumptions. To further enhance robustness, we design an incremental PID controller that dynamically adjusts the regularization coefficient of the core interaction matrix according to the training dynamics, allowing the optimization process to automatically balance model expressiveness and overfitting. Extensive experiments on real-world QoS datasets demonstrate that ACELF consistently outperforms several state-of-the-art methods in terms of prediction accuracy.