Journal on Communications

   

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

LIU Jianxun 1,2 , DING Linghang 1,2 , KANG Guosheng 1,2 , CAO Buqing 1,2 , XIAO Yong 1,2#br#   

  1. 1. Hunan Provincial Key Lab. for Services Computing and Novel Software Technology, HNUST, Xiangtan 411201, China
    2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

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 is proposeproposed with considering of the hidden environmental
preference information in QoS and the common feature s of multi multi-class QoS QoS. First, QoS data is modeled as a useruser-servicservice bipartite graph graph; then , multi multi-component graph convolution neural network is used for feature extraction and mapping, and
the weighted fusion method is used for the same dimensional mapping of multi multi-cl ass of QoS features. Subsequently, t he
attention factor decomposition machine is used to extract the first first-order features, second second-order interactive features features, and
highhigh-order interactive features of the mapped feature vector . the results of each par part ar e 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 ( and average absolute error ( MAE).

Key words: joint predi ction, quality of serviceervice, preference feature, deep fusion

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