Sharpness-aware Second-order Latent Factor Model for High-dimensional and Incomplete Data

• URL: http://arxiv.org/abs/2512.16277v1
• Date: Thu, 18 Dec 2025 07:57:27 GMT
• Title: Sharpness-aware Second-order Latent Factor Model for High-dimensional and Incomplete Data
• Authors: Jialiang Wang, Xueyan Bao, Hao Wu,
• Abstract summary: Second-order Latent Factor (SLF) model, a class of low-rank representation learning methods, has proven effective at extracting node-to-node data.<n>We propose Sharpness-aware SLF (SSLF) model to improve the generalization of industrial representation models.
• Score: 8.319118597967663
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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