ARRQP：基于图卷积的抗异常实时QoS预测框架
ARRQP: Anomaly Resilient Real-time QoS Prediction Framework with Graph Convolution
AI总结：
针对QoS预测中存在的离群点、数据稀疏、灰羊实例和冷启动等异常问题，提出融合图卷积、鲁棒损失与抗稀疏灰羊检测的ARRQP框架，在WS-DREAM数据集上实现了准确实时的预测。
摘要：
在现代面向服务架构领域，保障服务质量（Quality of Service, QoS）至关重要。提前预测QoS值的能力可帮助用户做出明智决策。然而，在存在离群点、数据稀疏、灰羊实例和冷启动场景等各类问题与异常的情况下，实现准确的QoS预测仍是一项挑战。当前最先进的方法在同时应对这些问题时往往存在不足，导致性能下降。本文提出一种实时QoS预测框架ARRQP，重点提升对数据中异常的鲁棒性。ARRQP利用图卷积技术的优势，即便在数据有限或稀疏的情况下，也能捕捉用户与服务之间复杂的关系和依赖。该框架融合了上下文信息与协同洞察，可全面理解用户-服务交互。通过使用鲁棒损失函数，ARRQP在模型训练期间有效降低了离群点的影响。此外，我们提出了一种抗稀疏的灰羊检测方法，随后对这类实例单独处理以进行QoS预测。我们还通过优先考虑上下文特征而非协同特征，解决了冷启动问题。在基准数据集WS-DREAM上的实验结果表明，该框架可有效实现准确、及时的QoS预测。
英文摘要：
In the realm of modern service-oriented architecture, ensuring Quality of Service (QoS) is of paramount importance. The ability to predict QoS values in advance empowers users to make informed decisions. However, achieving accurate QoS predictions in the presence of various issues and anomalies, including outliers, data sparsity, grey-sheep instances, and cold-start scenarios, remains a challenge. Current state-of-the-art methods often fall short when addressing these issues simultaneously, resulting in performance degradation. In this paper, we introduce a real-time QoS prediction framework (called ARRQP) with a specific emphasis on improving resilience to anomalies in the data. ARRQP utilizes the power of graph convolution techniques to capture intricate relationships and dependencies among users and services, even when the data is limited or sparse. ARRQP integrates both contextual information and collaborative insights, enabling a comprehensive understanding of user-service interactions. By utilizing robust loss functions, ARRQP effectively reduces the impact of outliers during the model training. Additionally, we introduce a sparsity-resilient grey-sheep detection method, which is subsequently treated separately for QoS prediction. Furthermore, we address the cold-start problem by emphasizing contextual features over collaborative features. Experimental results on the benchmark WS-DREAM dataset demonstrate the framework's effectiveness in achieving accurate and timely QoS predictions.
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。
