GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction

Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specif...

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Bibliographic Details
Published inarXiv.org
Main Authors Hu, Shengxiang, Zou, Guobing, Zhang, Bofeng, Wu, Shaogang, Lin, Shiyi, Gan, Yanglan, Chen, Yixin
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.09.2024
Subjects
Collaborative learning
Feature extraction
Multilayers
Predictions
Quality of service
Quality of service architectures
Service oriented architecture
User satisfaction
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Summary:Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments. However, current methods often neglect high-order latent collaborative relationships and fail to dynamically adjust feature learning for specific user-service invocations, which are critical for precise feature extraction within each time slice. Moreover, the prevalent use of RNNs for modeling temporal feature evolution patterns is constrained by their inherent difficulty in managing long-range dependencies, thereby limiting the detection of long-term QoS trends across multiple time slices. These shortcomings dramatically degrade the performance of temporal QoS prediction. To address the two issues, we propose a novel Graph Attention Collaborative Learning (GACL) framework for temporal QoS prediction. Building on a dynamic user-service invocation graph to comprehensively model historical interactions, it designs a target-prompt graph attention network to extract deep latent features of users and services at each time slice, considering implicit target-neighboring collaborative relationships and historical QoS values. Additionally, a multi-layer Transformer encoder is introduced to uncover temporal feature evolution patterns, enhancing temporal QoS prediction. Extensive experiments on the WS-DREAM dataset demonstrate that GACL significantly outperforms state-of-the-art methods for temporal QoS prediction across multiple evaluation metrics, achieving the improvements of up to 38.80%.
ISSN:2331-8422