标题： PSLF：一种融入PID控制器的二阶潜在因子分析模型用于推荐系统 显示英文标题

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

摘要： 基于二阶的潜在因子（SLF）分析模型在图表示学习中表现出色，特别是在高维且不完整（HDI）交互数据方面，通过引入损失函数曲率信息实现优势。然而，其目标函数通常是双线性且非凸的，导致SLF模型收敛速度较低。为了解决这一问题，本文提出了一种结合PID控制器的SLF（PSLF）模型，采用两个关键策略：a）通过引入PID控制器原理优化学习误差估计；b）通过Hessian-向量积获取二阶信息洞见。多项HDI数据集上的实验结果表明，所提出的PSLF模型在收敛速度和泛化性能方面优于四种基于先进优化器的最新潜在因子模型。 显示英文摘要

摘要： 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.