# DRSLF: Double Regularized Second-Order Low-Rank Representation for Web Service QoS Prediction

arXiv: https://arxiv.org/abs/2505.03822 · HTML: https://mlsanity.com/paper/2505.03822/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 87 references that are in the map.
- First posted on arXiv 3 May 2025.

## Builds on (most cited first, 1 of 1)

- [New insights and perspectives on the natural gradient method](https://mlsanity.com/paper/1412.1193/) (2014)

## Abstract (arXiv)

Quality-of-Service (QoS) data plays a crucial role in cloud service selection. Since users cannot access all services, QoS can be represented by a high-dimensional and incomplete (HDI) matrix. Latent factor analysis (LFA) models have been proven effective as low-rank representation techniques for addressing this issue. However, most LFA models rely on first-order optimizers and use L2-norm regularization, which can lead to lower QoS prediction accuracy. To address this issue, this paper proposes a double regularized second-order latent factor (DRSLF) model with two key ideas: a) integrating L1-norm and L2-norm regularization terms to enhance the low-rank representation performance; b) incorporating second-order information by calculating the Hessian-vector product in each conjugate gradient step. Experimental results on two real-world response-time QoS datasets demonstrate that DRSLF has a higher low-rank representation capability than two baselines.