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arXiv · 2409.00448

PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System

Jialiang Wang·Yan Xia·Ye Yuan
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Abstract

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.

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Jialiang Wang, Yan Xia, Ye Yuan. 2024-08-31. PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System. https://arxiv.org/abs/2409.00448

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