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AERQP: adaptive embedding representation-based QoS prediction for web service recommendation

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Abstract

Over the last few years, abundant and diverse Web services have migrated to the cloud. However, the disparity of the cloud environment renders quality of service (QoS) prediction harder. Based on analyzing the problems of inaccurate semantic representation and inadequate service invocation modeling in QoS prediction within the cloud, we propose an adaptive embedding representation-based QoS prediction method (AERQP) for Web services recommendation. First, the optimal embedding dimension of an explicit feature is determined dynamically by a policy network. Next, the embedding representation is remapped based on linear transformations. Then, global feature interactions are learned through a deep network with multi-head external attention as the core to fully model service invocations and realize accurate QoS prediction. Last, the experiment results indicate that AERQP improves an average of 44.8% and 16.9% on mean absolute error and root mean square error, respectively, compared to baseline methods.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the Industry-university Research Innovation Foundation of Ministry of Education of China under Grant 2021FNA01001.

Funding

The Industry-university Research Innovation Foundation of Ministry of Education of China under Grant 2021FNA01001.

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Authors and Affiliations

  1. College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, 266580, China

    Hongxia Zhang, Mengde Wu, Qiqi Feng & Hao Li

Authors
  1. Hongxia Zhang
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  2. Mengde Wu
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  3. Qiqi Feng
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  4. Hao Li
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Contributions

HZ wrote the main manuscript text. MW and QF worked together to process the dataset and build the experimental environment. HZ and MW collaborated on specific experiments. QF and HL cooperatively prepared Figs. 1–7. MW, QF, and HL performed the experimental analysis together. All authors reviewed the manuscript.

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Correspondence to Hongxia Zhang.

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Zhang, H., Wu, M., Feng, Q. et al. AERQP: adaptive embedding representation-based QoS prediction for web service recommendation. J Supercomput 80, 3042–3065 (2024). https://doi.org/10.1007/s11227-023-05582-9

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