DRSLF: Double Regularized Second-Order Low-Rank Representation for Web Service QoS Prediction
This work addresses QoS prediction for cloud service selection, but it appears incremental as it builds on existing latent factor analysis models with specific regularization and optimization improvements.
The paper tackled the problem of predicting Quality-of-Service (QoS) for web services from incomplete data by proposing a double regularized second-order latent factor model, which achieved higher low-rank representation capability than two baselines on real-world datasets.
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.