Aug 20, 2024 · We propose the Target-Prompt Online Graph Collaborative Learning (TOGCL) framework for temporal QoS prediction. It leverages a dynamic user-service invocation ...
Aug 20, 2024 · We propose TOGCL, a novel framework for temporal QoS prediction that incorporates a dynamic user-service invocation graph to capture intricate ...
The Target-Prompt Online Graph Collaborative Learning (TOGCL) framework for temporal QoS prediction leverages a dynamic user-service invocation graph to ...
Aug 21, 2024 · We propose a novel framework for temporal-aware QoS prediction by dynamic graph neural collaborative learning.
Accurate prediction of temporal QoS is crucial for maintaining service reliability and enhancing user satisfaction in dynamic service-oriented environments.
In service-oriented architecture, accurately predicting the Quality of Service (QoS) is vital for maintaining reliability and enhancing user satisfaction.
Target-Prompt Online Graph Collaborative Learning for ...
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Sep 13, 2024 · This paper presents a novel method called Target-Prompt Graph Attention Network (TPGAN) for temporal QoS prediction in web service invocation.
In service-oriented architecture, accurately predicting the Quality of Service (QoS) is vital for maintaining reliability and enhancing user satisfaction.
Target-Prompt Online Graph Collaborative Learning for Temporal QoS Prediction. 時間的QoS予測のための目標-目標オンライングラフ協調学習【JST機械翻訳】.
Building on this graph, it develops a target-prompt graph attention network to extract online deep latent features of users and services at each time slice, ...