用PID控制器加速推荐系统模型收敛，提升性能。
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System
- 引入PID控制思想优化学习误差估计
- 通过海森向量积获取二阶信息，加快收敛
- 在多个高维不完整数据集上表现优于先进模型
基于二阶的隐因子（SLF）分析模型在图表示学习中表现出色，尤其适用于高维不完整（HDI）交互数据，因其能利用损失曲面的曲率信息。然而，其目标函数通常为双线性且非凸，导致收敛速度慢。为此，本文提出一种融合PID控制器的SLF（PSLF）模型，采用两项关键策略：a）通过引入PID控制原理改进学习误差估计；b）利用海森向量积获取二阶信息。在多个HDI数据集上的实验结果表明，所提PSLF模型在收敛速度和泛化性能上均优于四种基于先进优化器的主流隐因子模型。
原文摘要 · Abstract (English)
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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