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	Momentum-incorporated latent factorization of tensors for extracting temporal patterns from QoS data
	Chen, Minzhi1; Wu, Hao2,3; He, Chunlin1; Chen, Sili1
	2019
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. © 2019 IEEE.
语种	英语
DOI	10.1109/SMC.2019.8914594
会议(录)名称	2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
页码	1757-1762
收录类别	EI
会议地点	Bari, Italy
会议日期	October 6, 2019 - October 9, 2019