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A Multi-stack Denoising Autoencoder for QoS Prediction

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Artificial Neural Networks and Machine Learning – ICANN 2022 (ICANN 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13530))

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Abstract

In the era of network information overload, personalized service recommendation is paid more and more attention by researchers, and Quality of Service (QoS) is a key criterion for service selection and recommendation. QoS is described as a non-functional attribute of Web services, so the prediction of QoS with high precision is an important means to realize personalized recommendation. In this paper, a Multi-stack Denoising Autoencoder (MSDAE) model is proposed to predict QoS. Firstly, the location information and the improved Jaccard similarity coefficient are used to obtain the trusted similar neighbors of users and services. Partially missing values of sparse user-service QoS matrix are pre-populated and users’ preference informations are filled. Then, MSDAE is used to learn and train the processed QoS matrix to predict the missing QoS. Finally, the experimental results on WSDream-dataset1 show that the proposed method predicts QoS with higher accuracy than other prediction methods.

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Change history

  • 08 December 2022

    In the version of this paper that was originally published the affiliation of the authors was incorrect. This has now been corrected.

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Acknowledgements

This work was supported in part by Shandong Province Key R &D Program (Major Science and Technology Innovation Project) Project under Grants 2020CXGC010102.

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Authors and Affiliations

  1. School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Mengwei Wu, Qin Lu & Yingxue Wang

Authors
  1. Mengwei Wu
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  2. Qin Lu
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  3. Yingxue Wang
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Corresponding author

Correspondence to Qin Lu .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, UK

    Elias Pimenidis

  2. Lancaster University, Lancaster, UK

    Plamen Angelov

  3. Digital Innovation, Teeside University, Middlesbrough, UK

    Chrisina Jayne

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  5. The University of the West of England, Bristol, UK

    Mehmet Aydin

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Wu, M., Lu, Q., Wang, Y. (2022). A Multi-stack Denoising Autoencoder for QoS Prediction. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_62

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  • DOI: https://doi.org/10.1007/978-3-031-15931-2_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15930-5

  • Online ISBN: 978-3-031-15931-2

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