改进云服务质量预测，用双正则化+二阶信息提升精度
DRSLF: Double Regularized Second-Order Low-Rank Representation for Web Service QoS Prediction
- 结合L1与L2正则化，增强低秩表示能力
- 在共轭梯度中引入海森向量积，捕捉二阶信息
- 在两个真实数据集上优于基线模型
服务质量（QoS）数据在云服务选择中至关重要。由于用户无法访问所有服务，QoS通常表现为高维不完整（HDI）矩阵。潜在因子分析（LFA）模型作为低秩表示技术已被证明有效。然而，多数LFA模型依赖一阶优化器并使用L2范数正则化，导致预测精度较低。为此，本文提出双正则化二阶潜在因子（DRSLF）模型，包含两个关键思想：a）整合L1与L2正则化项以提升低秩表示性能；b）通过在每步共轭梯度计算中引入海森向量积，融入二阶信息。在两个真实响应时间QoS数据集上的实验表明，DRSLF的低秩表示能力优于两个基线模型。
原文摘要 · Abstract (English)
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.
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