Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation
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 specially designed non-negative training schemes, which lead to low compatibility and sc...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-03-01
|
| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024064/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841540049462624256 |
|---|---|
| author | Mingwei LIN Wenqiang LI Xiuqin XU Jian LIU |
| author_facet | Mingwei LIN Wenqiang LI Xiuqin XU Jian LIU |
| author_sort | Mingwei LIN |
| collection | DOAJ |
| description | 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 specially designed non-negative training schemes, which lead to low compatibility and scalability, an accelerated unconstrained 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 computational efficiency and estimation accuracy than the state-of-the-art QoS estimation models. |
| format | Article |
| id | doaj-art-75e1b783fcbe450db7fb290612387b53 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-75e1b783fcbe450db7fb290612387b532025-01-14T06:21:56ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-03-014516618159296691Accelerated unconstrained latent factorization of tensor model for Web service QoS estimationMingwei LINWenqiang LIXiuqin XUJian LIUAiming 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 specially designed non-negative training schemes, which lead to low compatibility and scalability, an accelerated unconstrained 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 computational efficiency and estimation accuracy than the state-of-the-art QoS estimation models.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024064/quality of servicelatent factorization analysisnon-negative latent factorization of tensor modelunconstrained non-negativemomentum method |
| spellingShingle | Mingwei LIN Wenqiang LI Xiuqin XU Jian LIU Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation Tongxin xuebao quality of service latent factorization analysis non-negative latent factorization of tensor model unconstrained non-negative momentum method |
| title | Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation |
| title_full | Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation |
| title_fullStr | Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation |
| title_full_unstemmed | Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation |
| title_short | Accelerated unconstrained latent factorization of tensor model for Web service QoS estimation |
| title_sort | accelerated unconstrained latent factorization of tensor model for web service qos estimation |
| topic | quality of service latent factorization analysis non-negative latent factorization of tensor model unconstrained non-negative momentum method |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024064/ |
| work_keys_str_mv | AT mingweilin acceleratedunconstrainedlatentfactorizationoftensormodelforwebserviceqosestimation AT wenqiangli acceleratedunconstrainedlatentfactorizationoftensormodelforwebserviceqosestimation AT xiuqinxu acceleratedunconstrainedlatentfactorizationoftensormodelforwebserviceqosestimation AT jianliu acceleratedunconstrainedlatentfactorizationoftensormodelforwebserviceqosestimation |