  In service-oriented architectures, accurately predicting the Quality of
Service (QoS) is crucial for maintaining reliability and enhancing user
satisfaction. However, significant challenges remain due to existing methods
always overlooking high-order latent collaborative relationships between users
and services and failing to dynamically adjust feature learning for every
specific user-service invocation, which are critical for learning accurate
features. Additionally, reliance on RNNs for capturing QoS evolution hampers
models' ability to detect long-term trends due to difficulties in managing
long-range dependencies. To address these challenges, we propose the
\underline{T}arget-Prompt \underline{O}nline \underline{G}raph
\underline{C}ollaborative \underline{L}earning (TOGCL) framework for
temporal-aware QoS prediction. TOGCL leverages a dynamic user-service
invocation graph to model historical interactions, providing a comprehensive
representation of user-service relationships. 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, simultaneously considering
implicit collaborative relationships between target users/services and their
neighbors, as well as relevant historical QoS values. Additionally, a
multi-layer Transformer encoder is employed to uncover temporal feature
evolution patterns of users and services, leading to temporal-aware QoS
prediction. Extensive experiments conducted on the WS-DREAM dataset demonstrate
that our proposed TOGCL framework significantly outperforms state-of-the-art
methods across multiple metrics, achieving improvements of up to 38.80\%. These
results underscore the effectiveness of the TOGCL framework for precise
temporal QoS prediction.
