TY - GEN
T1 - A Generalized Nesterov-Accelerated Hessian-Vector-Based Latent Factor Analysis Model for QoS Prediction
AU - Li, Weiling
AU - Luo, Xin
AU - Zhou, Meng Chu
N1 - Funding Information:
*This research is supported in part by CAAI-Huawei MindSpore Open Fund under grant CAAIXSJLJJ-2020-004B, in part by Chongqing Research Program of Technology Innovation and Application under grant cstc2019jscx-fxydX0027, is supported in part by the Guangdong United Foundation of Basic Research and Application under grants 2019A1515111058, and in part by the China Postdoctoral Science Foundation funded project under grants 2020M683293 (Corresponding Authors: X. Luo and M. Zhou).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/9
Y1 - 2021/9
N2 - User-side Quality-of-Service (QoS) data are vital for efficient cloud service selection, while available QoS data in real applications are commonly described by a high-dimensional and Sparse (HiDS) matrix due to a) increasing users and services, and b) the impossibility of observing the full invoking mapping among users and services. A latent factor analysis (LFA) model has proven to be efficient in performing representation learning on such an HiDS QoS matrix. However, existing LFA models commonly adopt a first-order optimizer that cannot makes it approach the second-order stationary point of the learning objective, thereby resulting in accuracy loss. Aiming at addressing this issue, this study proposes to incorporate a Generalized Nesterov's Acceleration (GNA) method in to a Hessian-vector algorithm for LFA, thereby establishing a GNA-incorporated Hessian-vector-based LFA (GNHL) model with two-fold ideas: A) adopting the principle of a Hessian-vector method to acquire a proper Newton step efficiently, and b) incorporating a GNA method into its linear search for accelerating its convergence rate. Experimental results on six real QoS datasets demonstrate that a GNHL model outperforms state-of-The-Art LFA models in generating highly accurate predictions for missing QoS data with low computational burden.
AB - User-side Quality-of-Service (QoS) data are vital for efficient cloud service selection, while available QoS data in real applications are commonly described by a high-dimensional and Sparse (HiDS) matrix due to a) increasing users and services, and b) the impossibility of observing the full invoking mapping among users and services. A latent factor analysis (LFA) model has proven to be efficient in performing representation learning on such an HiDS QoS matrix. However, existing LFA models commonly adopt a first-order optimizer that cannot makes it approach the second-order stationary point of the learning objective, thereby resulting in accuracy loss. Aiming at addressing this issue, this study proposes to incorporate a Generalized Nesterov's Acceleration (GNA) method in to a Hessian-vector algorithm for LFA, thereby establishing a GNA-incorporated Hessian-vector-based LFA (GNHL) model with two-fold ideas: A) adopting the principle of a Hessian-vector method to acquire a proper Newton step efficiently, and b) incorporating a GNA method into its linear search for accelerating its convergence rate. Experimental results on six real QoS datasets demonstrate that a GNHL model outperforms state-of-The-Art LFA models in generating highly accurate predictions for missing QoS data with low computational burden.
KW - Cloud Service
KW - Gauss-Newton approximation
KW - Generalized Nesterov's acceleration
KW - Latent factor analysis
KW - Machine learning
KW - Quality-of-Service (QoS)
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U2 - 10.1109/CLOUD53861.2021.00033
DO - 10.1109/CLOUD53861.2021.00033
M3 - Conference contribution
AN - SCOPUS:85119330029
T3 - IEEE International Conference on Cloud Computing, CLOUD
SP - 200
EP - 205
BT - Proceedings - 2021 IEEE 14th International Conference on Cloud Computing, CLOUD 2021
A2 - Ardagna, Claudio Agostino
A2 - Chang, Carl K.
A2 - Daminai, Ernesto
A2 - Ranjan, Rajiv
A2 - Wang, Zhongjie
A2 - Ward, Robert
A2 - Zhang, Jia
A2 - Zhang, Wensheng
PB - IEEE Computer Society
T2 - 14th IEEE International Conference on Cloud Computing, CLOUD 2021
Y2 - 5 September 2021 through 11 September 2021
ER -