TY - JOUR
T1 - Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors
AU - Luo, Xin
AU - Wu, Hao
AU - Yuan, Huaqiang
AU - Zhou, Meng Chu
N1 - Funding Information:
Manuscript received January 13, 2019; revised February 24, 2019; accepted March 2, 2019. Date of publication April 4, 2019; date of current version April 15, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61772493, Grant 91646114, and Grant 61602352, in part by the Chongqing Research Program of Technology Innovation and Application under Grant cstc2017rgzn-zdyfX0020, Grant cstc2017zdcy-zdyf0554, and Grant cstc2017rgzn-zdyf0118, in part by the Chongqing Cultivation Program of Innovation and Entrepreneurship Demonstration Group under Grant cstc2017kjrc-cxcytd0149, in part by the Chongqing Overseas Scholars Innovation Program under Grant cx2017012 and Grant cx2018011, and in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences. This paper was recommended by Associate Editor D. Tao. (Xin Luo and Hao Wu are co-first authors.) (Corresponding authors: Huaqiang Yuan; MengChu Zhou.) X. Luo is with the School of Computer Science and Technology, Dongguan University of Technology, Dongguan, Guangdong 523808, China, also with the Chongqing Engineering Research Center of Big Data Application for Smart Cities, Chinese Academy of Sciences, Chongqing 400714, China, and also with the Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China (e-mail: luoxin21@gmail.com).
Publisher Copyright:
© 2013 IEEE.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - Latent factor analysis (LFA)
KW - latent factorization of tensor
KW - learning temporal pattern
KW - linear bias (LB)
KW - non-negative latent factorization of tensor
KW - non-negativity constraint
KW - quality-of-service (QoS) prediction
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U2 - 10.1109/TCYB.2019.2903736
DO - 10.1109/TCYB.2019.2903736
M3 - Article
C2 - 30969935
AN - SCOPUS:85083909672
SN - 2168-2267
VL - 50
SP - 1798
EP - 1809
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 5
M1 - 8681714
ER -