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Momentum-incorporated latent factorization of tensors for extracting temporal patterns from QoS data

文献类型：会议论文

作者Chen, Minzhi3; Wu, Hao1,2; He, Chunlin3; Chen, Sili3
出版日期2019
会议日期October 6, 2019 - October 9, 2019
会议地点Bari, Italy
DOI10.1109/SMC.2019.8914594
页码1757-1762
英文摘要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.
会议录2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
语种英语
ISSN号1062922X
源URL[http://119.78.100.138/handle/2HOD01W0/9784]  
专题中国科学院重庆绿色智能技术研究院
作者单位1.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chinese Academy of Sciences, Chongqing Institute of Green and Intelligent Technology, Chongqing; 400714, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China
3.Computer School of China West Normal University, Nanchong, Sichuan; 637002, China;
推荐引用方式
GB/T 7714		 Chen, Minzhi,Wu, Hao,He, Chunlin,et al. Momentum-incorporated latent factorization of tensors for extracting temporal patterns from QoS data[C]. 见:. Bari, Italy. October 6, 2019 - October 9, 2019.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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