通信学报2024，Vol.45Issue(3)：166-181,16.DOI:10.11959/j.issn.1000-436x.2024064
基于加速无约束张量隐因子分解模型的Web服务QoS估计
Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation
摘要
Abstract
Aiming at the problem that the Web service quality of service(QoS)estimation methods based on the non-negative latent factorization of tensor model(NLFT)depend heavily on non-negative initial random data and spe-cially designed non-negative training schemes,which lead to low compatibility and scalability,an accelerated uncon-strained latent factorization of tensor(AULFT)model was proposed.The proposed model consisted of three main parts.The non-negative constraints from decision parameters were transferred to output latent factors and they were connected through the single-element-dependent mapping function.A momentum-incorporated stochastic gradient descent(MSGD)algorithm was used to effectively improve the convergence rate and estimation accuracy of the proposed AULFT model.The detailed algorithm and result analysis of the proposed AULFT model were presented.The empirical study on two dynamic QoS datasets in real industrial applications demonstrates that the proposed AULFT model has higher computa-tional efficiency and estimation accuracy than the state-of-the-art QoS estimation models.关键词
服务质量/隐因子分解分析/张量非负隐因子分解模型/无约束非负/动量方法Key words
quality of service/latent factorization analysis/non-negative latent factorization of tensor model/uncon-strained non-negative/momentum method分类
电子信息工程引用本文复制引用
林铭炜,李文强,许秀琴,刘健..基于加速无约束张量隐因子分解模型的Web服务QoS估计[J].通信学报,2024,45(3):166-181,16.基金项目
国家自然科学基金资助项目(No.62272103) （No.62272103）
福建省自然科学基金杰青项目资助项目(No.2022J06020) （No.2022J06020）
福建省"雏鹰计划"青年拔尖人才计划基金资助项目(No.F21E0011202B01) The National Natural Science Foundation of China(No.62272103),Distinguished Young Project of Natural Sci-ence Foundation of Fujian Province(No.2022J06020),The Young Top Talent of Young Eagle Program of Fujian Province(No.F21E0011202B01) （No.F21E0011202B01）
