Joint QoS prediction for Web services based on deep fusion of features

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Joint QoS prediction for Web services based on deep fusion of features

Correspondences | 更新时间：2024-06-05

- Joint QoS prediction for Web services based on deep fusion of features
- Journal on Communications Vol. 43, Issue 7, Pages: 215-226(2022)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61872139);Educational Commission of Hunan Prov-ince of China(20B244)
- DOI：10.11959/j.issn.1000-436x.2022107
  CLC： TN92
- Online First：2022-06，
  
  Published：25 July 2022
- 稿件说明：
移动端阅览
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：

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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