QoSDiff: 一种利用去噪扩散和对抗注意力的隐式拓扑嵌入学习框架，用于鲁棒的QoS预测
QoSDiff: An Implicit Topological Embedding Learning Framework Leveraging Denoising Diffusion and Adversarial Attention for Robust QoS Prediction
AI总结：
QoSDiff通过去噪扩散和对抗注意力机制，实现隐式拓扑关系建模，提升QoS预测的鲁棒性和泛化能力。
摘要：
准确的服务质量（QoS）预测是服务计算的基础，为服务选择提供必要的数据驱动指导，并确保卓越的用户体验。然而，普遍的方法，特别是图神经网络（GNNs），严重依赖于构建显式用户-服务交互图。这种依赖不仅导致在大规模场景中显式图构建的不可行性，还限制了隐式拓扑关系的建模，并加剧了对环境噪声和异常值的敏感性。为了解决这些挑战，本文介绍了一种新的嵌入学习框架QoSDiff，该框架不需要显式图构建。具体来说，它利用去噪扩散概率模型从噪声初始状态中恢复内在的潜在结构。为进一步捕捉高阶相互作用，我们提出了一种对抗性交互模块，该模块集成了双向混合注意力机制。这种对抗范式动态区分信息模式与噪声，使复杂用户-服务关联的双视角建模成为可能。在两个大规模真实世界数据集上的大量实验表明，QoSDiff显著优于最先进的基线。值得注意的是，结果突显了该框架在跨数据集泛化能力和对观测噪声的卓越鲁棒性。
英文摘要：
Accurate Quality of Service (QoS) prediction is fundamental to service computing, providing essential data-driven guidance for service selection and ensuring superior user experiences. However, prevalent approaches, particularly Graph Neural Networks (GNNs), heavily rely on constructing explicit user--service interaction graphs. Such reliance not only leads to the intractability of explicit graph construction in large-scale scenarios but also limits the modeling of implicit topological relationships and exacerbates susceptibility to environmental noise and outliers. To address these challenges, this paper introduces \emph{QoSDiff}, a novel embedding learning framework that bypasses the prerequisite of explicit graph construction. Specifically, it leverages a denoising diffusion probabilistic model to recover intrinsic latent structures from noisy initializations. To further capture high-order interactions, we propose an adversarial interaction module that integrates a bidirectional hybrid attention mechanism. This adversarial paradigm dynamically distinguishes informative patterns from noise, enabling a dual-perspective modeling of intricate user--service associations. Extensive experiments on two large-scale real-world datasets demonstrate that QoSDiff significantly outperforms state-of-the-art baselines. Notably, the results highlight the framework's superior cross-dataset generalization capability and exceptional robustness against observational noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
