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.

翻译：最近，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扮演着至关重要的角色。决定服务性能的服务质量（QoS）参数通常用于推荐，且随时间波动。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。现有的时态QoS预测方法由于各种局限性，如无法处理数据稀疏性和异常值，以及捕捉用户-服务交互中的高阶时态关系，很难达到预期的准确性。尽管一些基于循环神经网络的架构能够对QoS数据中的时态关系进行建模，但由于缺乏其他特征（例如协同特征）来理解用户-服务交互之间的关系，预测准确性会下降。本文针对上述挑战，提出了一种基于多源协同特征的可扩展时态QoS预测策略（TPMCF），实现了高预测精度和快速响应。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，从时空维度自动提取特征，从而融合用户/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在预测精度上优于主流最先进方法，同时确保了高可扩展性和相当快的响应速度。