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	Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors
	Luo, Xin1,2,3; Wu, Hao2,3; Yuan, Huaqiang1; Zhou, MengChu4,5
	2020-05-01
摘要	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.
关键词	Quality of service Hidden Markov models Data models Training Web services Time factors Latent factor analysis (LFA) latent factorization of tensor learning temporal pattern linear bias (LB) non-negative latent factorization of tensor non-negativity constraint quality-of-service (QoS) prediction
DOI	10.1109/TCYB.2019.2903736
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	50期号:5页码:1798-1809
通讯作者	Yuan, Huaqiang(yuanhq@dgut.edu.cn) ; Zhou, MengChu(zhou@njit.edu)
收录类别	SCI
WOS记录号	WOS:000528622000002
语种	英语