DRSLF: Double Regularized Second-Order Low-Rank Representation for Web Service QoS Prediction
By: Hao Wu, Jialiang Wang
Potential Business Impact:
Makes cloud services pick the best options.
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.
Similar Papers
Dynamic QoS Prediction via a Non-Negative Tensor Snowflake Factorization
Machine Learning (CS)
Predicts what services people will like.
Federated Latent Factor Model for Bias-Aware Recommendation with Privacy-Preserving
Machine Learning (CS)
Keeps your private data safe while recommending things.
FairLRF: Achieving Fairness through Sparse Low Rank Factorization
Machine Learning (CS)
Makes AI fairer without losing accuracy.