基于特征深度融合的Web服务QoS联合预测

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基于特征深度融合的Web服务QoS联合预测

学术通信 | 更新时间：2024-06-05

- 基于特征深度融合的Web服务QoS联合预测
- Joint QoS prediction for Web services based on deep fusion of features
- 通信学报 2022年43卷第7期页码：215-226
- 作者机构：
  
  1. 湖南科技大学服务计算与软件新技术湖南省重点实验室，湖南湘潭 411201
  2. 湖南科技大学计算机科学与工程学院，湖南湘潭 411201
- 作者简介：
  
  [ "刘建勋（1970- ），男，湖南衡阳人，博士，湖南科技大学教授，主要研究方向为工作流管理、服务计算、云计算、语义和知识网格等" ]
  [ "丁领航（1994- ），男，湖南湘潭人，湖南科技大学硕士生，主要研究方向为服务计算与云计算" ]
  [ "康国胜（1985- ），男，湖南郴州人，博士，湖南科技大学讲师，主要研究方向为服务计算和云计算、以数据为中心的业务流程管理、业务流程配置、数据挖掘和社交网络" ]
  [ "曹步清（1979- ），男，湖南湘潭人，博士，湖南科技大学教授，主要研究方向为服务计算、社交网络和软件工程" ]
  [ "肖勇（1995- ），男，湖南湘潭人，湖南科技大学博士生，主要研究方向为服务推荐、服务集群和网络表示学习" ]
- 基金信息：
  
  国家自然科学基金资助项目(61872139);湖南省教育厅基金资助项目(20B244)
- DOI：10.11959/j.issn.1000-436x.2022107
  中图分类号： TN92
- 网络首发：2022-06，
  
  纸质出版：2022-07-25
- 稿件说明：
移动端阅览
刘建勋, 丁领航, 康国胜, 等. 基于特征深度融合的Web服务QoS联合预测[J]. 通信学报, 2022,43(7):215-226.

Jianxun LIU, Linghang DING, Guosheng KANG, et al. Joint QoS prediction for Web services based on deep fusion of features[J]. Journal on Communications, 2022, 43(7): 215-226.
刘建勋, 丁领航, 康国胜, 等. 基于特征深度融合的Web服务QoS联合预测[J]. 通信学报, 2022,43(7):215-226. DOI： 10.11959/j.issn.1000-436x.2022107.

Jianxun LIU, Linghang DING, Guosheng KANG, et al. Joint QoS prediction for Web services based on deep fusion of features[J]. Journal on Communications, 2022, 43(7): 215-226. DOI： 10.11959/j.issn.1000-436x.2022107.

摘要

为了解决Web服务QoS预测准确度不够的问题，针对QoS中隐藏的环境偏好信息和多类QoS隐藏的共同特征，提出一种基于特征深度融合的Web服务QoS联合预测方法。考虑QoS数据可以建模为用户-服务二部图，采用多组件图卷积神经网络进行特征提取和映射，采用加权融合方法对多类QoS特征进行同维映射。使用注意力因子分解机对映射后的特征向量进行一阶特征、二阶交互特征和高阶交互特征的提取，并结合各部分结果实现QoS联合预测。实验结果表明，所提方法在均方根误差和平均绝对误差方面优于现有QoS预测方法。

Abstract

In order to solve the problem of insufficient accuracy of Web service QoS prediction

a joint QoS prediction method for Web services based on the deep fusion of features was proposed with considering of the hidden environmental preference information in QoS and the common features of multi-class QoS.First

QoS data was modeled as a user-service bipartite graph and multi-component graph convolution neural network was used for feature extraction and mapping

and the weighted fusion method was used for the same dimensional mapping of multi-class of QoS features.Subsequently

the attention factor decomposition machine was used to extract the first-order features

second-order interactive features

and high-order interactive features of the mapped feature vector.Finally

the results of each part were combined to achieve the joint QoS prediction.The experimental results show that the proposed method is superior to the existing QoS prediction methods in terms of root mean square error (RMSE) and average absolute error (MAE).

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references

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