Second-order Latent Factor (SLF) model, a class of low-rank representation learning methods, has proven effective at extracting node-to-node interaction patterns from High-dimensional and Incomplete (HDI) data. However, its optimization is notoriously difficult due to its bilinear and non-convex nature. Sharpness-aware Minimization (SAM) has recently proposed to find flat local minima when minimizing non-convex objectives, thereby improving the generalization of representation-learning models. To address this challenge, we propose a Sharpness-aware SLF (SSLF) model. SSLF embodies two key ideas: (1) acquiring second-order information via Hessian-vector products; and (2) injecting a sharpness term into the curvature (Hessian) through the designed Hessian-vector products. Experiments on multiple industrial datasets demonstrate that the proposed model consistently outperforms state-of-the-art baselines.

翻译：二阶潜在因子模型作为一类低秩表示学习方法，已被证明能有效从高维不完整数据中提取节点间交互模式。然而，由于其双线性和非凸特性，其优化过程极为困难。锐度感知最小化方法近期被提出用于在最小化非凸目标时寻找平坦局部极小值，从而提升表示学习模型的泛化能力。为应对这一挑战，我们提出一种锐度感知二阶潜在因子模型。该模型包含两个核心思想：（1）通过海森-向量积获取二阶信息；（2）通过设计的海森-向量积将锐度项注入曲率（海森矩阵）中。在多个工业数据集上的实验表明，所提模型始终优于当前最先进的基线方法。