Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors
By Xin Luo, Hao Wu, Huaqiang Yuan, Mengchu Zhou. IEEE Transactions on Cybernetics, 2020
325 citations recorded by the source corpus.
Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly defined on static QoS data without the consideration of such temporal dynamics. To address this issue, this paper presents a biased non-negative latent factorization of tensors (BNLFTs) model for temporal pattern-aware QoS prediction. Its main idea is fourfold: 1) incorporating linear biases into the model for describing QoS fluctuations; 2) constraining the model to be non-negative for describing QoS non-negativity; 3) deducing a single LF-dependent, non-negative, and multiplicative update scheme for training the model; and 4) incorporating an alternating direction method into the model for faster convergence. The empirical studies on two dynamic QoS datasets from real applications show that compared with the state-of-the-art QoS-predictors, BNLFT represents temporal patterns more precisely with high computational efficiency, thereby achieving the most accurate predictions for missing QoS data.
Subfields: Computational Mathematics, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/ccaa497a82c39ba1ab3ca4d7509f3969a737b7e0
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Citations in the corpus
- Position-Transitional Particle Swarm Optimization-Incorporated Latent Factor Analysis (2022, methodology): “For a tested model, root mean squared error (RMSE) and mean absolute error (MAE) are frequently used to validate a tested model’s prediction accuracy for missing data of an HiDS matrix [4, 12, 13, 16, 21, 22, 47, 48]: where Γ denotes the testing set, which is disjoint with Λ and Ω .”