PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System
This work addresses a specific bottleneck in graph representation learning for recommender systems, offering an incremental improvement over existing methods.
The paper tackled the low convergence rate of second-order latent factor models in recommender systems by incorporating PID controller principles and Hessian-vector products, resulting in improved convergence rates and generalization performance on high-dimensional incomplete datasets.
A second-order-based latent factor (SLF) analysis model demonstrates superior performance in graph representation learning, particularly for high-dimensional and incomplete (HDI) interaction data, by incorporating the curvature information of the loss landscape. However, its objective function is commonly bi-linear and non-convex, causing the SLF model to suffer from a low convergence rate. To address this issue, this paper proposes a PID controller-incorporated SLF (PSLF) model, leveraging two key strategies: a) refining learning error estimation by incorporating the PID controller principles, and b) acquiring second-order information insights through Hessian-vector products. Experimental results on multiple HDI datasets indicate that the proposed PSLF model outperforms four state-of-the-art latent factor models based on advanced optimizers regarding convergence rates and generalization performance.