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QoS Prediction for Service Recommendation with Deep Feature Learning in Edge Computing Environment

  • Yuyu Yin
  • Lu Chen
  • Yueshen XuEmail author
  • Jian Wan
  • He Zhang
  • Zhida Mai
Article
  • 47 Downloads

Abstract

Along with the popularity of intelligent services and mobile services, service recommendation has become a key task, especially the task based on quality-of-service (QoS) in edge computing environment. Most existing service recommendation methods have some serious defects, and cannot be directly adopted in edge computing environment. For example, most of existing methods cannot learn deep features of users or services, but in edge computing environment, there are a variety of devices with different configurations and different functions, and it is necessary to learn deep features behind those complex devices. In order to fully utilize hidden features, this paper proposes a new matrix factorization (MF) model with deep features learning, which integrates a convolutional neural network (CNN). The proposed mode is named Joint CNN-MF (JCM). JCM is capable of using the learned deep latent features of neighbors to infer the features of a user or a service. Meanwhile, to improve the accuracy of neighbors selection, the proposed model contains a novel similarity computation method. CNN learns the neighbors features, forms a feature matrix and infers the features of the target user or target service. We conducted experiments on a real-world service dataset under a batch of cases of data densities, to reflect the complex invocation cases in edge computing environment. The experimental results verify that compared to counterpart methods, our method can consistently achieve higher QoS prediction results.

Keywords

Service recommendation QoS prediction Edge computing Deep feature learning Convolutional neural network Matrix factorization 

Notes

Acknowledgements

This paper is supported by the National Key Research and Development Program of China (No.2017YFB1400601), National Natural Science Foundation of China (No. 61872119, No. 61702391), Natural Science Foundation of Zhejiang Province (No. LY16F020017) and Shaanxi Province (No.2018JQ6050), and Fundamental Research Funds for Central Universities (JBX171007).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Yuyu Yin
    • 1
    • 2
  • Lu Chen
    • 1
  • Yueshen Xu
    • 3
    • 4
    Email author
  • Jian Wan
    • 2
    • 5
  • He Zhang
    • 3
  • Zhida Mai
    • 6
  1. 1.School of ComputerHangzhou Dianzi UniversityHangzhouChina
  2. 2.Key Laboratory of Complex Systems Modeling and Simulation of Ministry of EducationHangzhouChina
  3. 3.School of Computer Science and TechnologyXidian UniversityXi’anChina
  4. 4.Provincial Key Laboratory for Computer Information Processing TechnologySoochow UniversitySuzhouChina
  5. 5.School of Information and Electronic EngineeringZhejiang University of Science and TechnologyHangzhouChina
  6. 6.Xanten Guangdong Development Co., LtdFoshanChina

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