Time-aware QoS prediction for multi-task graph attention networks

Hefei Tan; Rong Zong; Hao Wu

doi:10.1117/12.2641092

13 October 2022 Time-aware QoS prediction for multi-task graph attention networks

Hefei Tan, Rong Zong, Hao Wu

Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871Q (2022) https://doi.org/10.1117/12.2641092
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China

Abstract

Web services have been integrated into every aspect of people's lives, and both online shopping and entertainment have extremely high requirements for Web service quality, so it is crucial to design a prediction algorithm that meets users' quality Web service quality for user experience. Existing algorithms mainly rely on the contextual information of users and services, without using the hidden graph structure information between users and services, and without considering the association between service quality attribute values, resulting in poor prediction accuracy for sparse data. To address this situation, a model based on graph attention network to capture information between users and services is proposed, and combined with MMOE multi-task learning to simultaneously complete the prediction of two quality of service attribute values simultaneously.

Citation Download Citation

Hefei Tan, Rong Zong, and Hao Wu "Time-aware QoS prediction for multi-task graph attention networks", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871Q (13 October 2022); https://doi.org/10.1117/12.2641092

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