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	A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction
	Wang, Qingxian1; Chen, Minzhi2; Shang, Mingsheng3,4,5; Luo, Xin3,4,5
	2019-11-20
摘要	Quality-of-service (QoS) of Web services vary over time, making it a significant issue to discover temporal patterns from them for addressing various subsequent analyzing tasks like missing QoS prediction. A Latent factorization of tensors (LFT)-based approach proves to be highly efficient in addressing this issue, which can be built through a stochastic gradient descent (SGD) solver efficiently. However, an SGD-based LFT model frequently suffers low-tail convergence. For addressing this issue, we present a momentum-incorporated latent factorization of tensors (MLFT) model, which integrates a momentum method into an SGD-based LFT model, thereby improving its convergence rate as well as maintaining the prediction accuracy for missing QoS data. Empirical studies on two dynamic industrial QoS datasets show that compared with an SGD-based LFT model, an MLFT model achieves faster convergence rate and higher prediction accuracy. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Big Data QoS prediction Temporal-aware QoS prediction Stochastic gradient descent Latent factorization of tensors Momentum method
DOI	10.1016/j.neucom.2019.08.026
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	367页码:299-307
收录类别	SCI
WOS记录号	WOS:000489017500028
语种	英语