GACL: Graph Attention Collaborative Learning for Temporal QoS Prediction

TL;DR

This paper introduces GACL, a novel framework combining graph attention and Transformer models to improve temporal QoS prediction by capturing high-order relationships and long-term trends, outperforming existing methods.

Contribution

The paper proposes a new GACL framework that models historical interactions with a dynamic graph and uses attention and Transformer mechanisms for enhanced feature extraction and temporal trend detection.

Findings

GACL outperforms state-of-the-art methods by up to 38.80% on WS-DREAM dataset.

It effectively captures high-order collaborative relationships.

It improves long-term QoS trend detection.

Abstract

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%.