TPMCF：使用多源协同特征的时序QoS预测
TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features
AI总结：
针对时序QoS预测中数据稀疏、异常值及高阶时序关系捕捉不足的问题，提出TPMCF方法，融合协同特征与时空自动提取特征，在WS-DREAM-2数据集上实现高准确性和高可扩展性。
摘要：
近年来，随着服务API的快速部署，个性化服务推荐在电子商务行业的发展中发挥了至关重要的作用。决定服务性能的服务质量（QoS）参数通常用于推荐，但这些参数会随时间波动。因此，QoS预测对于在功能等效的服务中随时间识别合适的服务至关重要。当前的时序QoS预测方法由于各种限制，如无法处理数据稀疏性和异常值，以及无法捕捉用户-服务交互中的高阶时序关系，难以达到理想的准确性。尽管一些最近的基于循环神经网络的架构能够建模QoS数据中的时序关系，但由于缺乏其他特征（如协同特征）来理解用户-服务交互之间的关系，预测准确性会降低。本文解决了上述挑战，并提出了一种可扩展的基于多源协同特征的时序QoS预测策略（TPMCF），实现了高预测准确性和更快的响应速度。TPMCF通过利用用户-服务关系，结合图卷积和带有多头自注意力的Transformer编码器自动提取的时空特征，融合了用户/服务的协同特征。我们在WS-DREAM-2数据集上验证了所提出的方法。大量实验表明，TPMCF在预测准确性方面优于主要的现有方法，同时确保了高可扩展性和相当快的响应速度。
英文摘要：
Recently, with the rapid deployment of service APIs, personalized service recommendations have played a paramount role in the growth of the e-commerce industry. Quality-of-Service (QoS) parameters determining the service performance, often used for recommendation, fluctuate over time. Thus, the QoS prediction is essential to identify a suitable service among functionally equivalent services over time. The contemporary temporal QoS prediction methods hardly achieved the desired accuracy due to various limitations, such as the inability to handle data sparsity and outliers and capture higher-order temporal relationships among user-service interactions. Even though some recent recurrent neural-network-based architectures can model temporal relationships among QoS data, prediction accuracy degrades due to the absence of other features (e.g., collaborative features) to comprehend the relationship among the user-service interactions. This paper addresses the above challenges and proposes a scalable strategy for Temporal QoS Prediction using Multi-source Collaborative-Features (TPMCF), achieving high prediction accuracy and faster responsiveness. TPMCF combines the collaborative-features of users/services by exploiting user-service relationship with the spatio-temporal auto-extracted features by employing graph convolution and transformer encoder with multi-head self-attention. We validated our proposed method on WS-DREAM-2 datasets. Extensive experiments showed TPMCF outperformed major state-of-the-art approaches regarding prediction accuracy while ensuring high scalability and reasonably faster responsiveness.
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。
