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：近期，随着服务API的快速部署，个性化服务推荐在电子商务行业增长中发挥着至关重要的作用。决定服务性能的服务质量（QoS）参数常被用于推荐，其数值随时间波动。因此，在功能等价的服务中，随时间变化识别合适服务的QoS预测至关重要。现有时间感知QoS预测方法由于存在数据稀疏与异常值处理能力不足、难以捕捉用户-服务交互中的高阶时序关系等局限，难以达到理想精度。尽管部分基于循环神经网络的最新架构可建模QoS数据的时序关系，但因缺乏其他特征（如协同特征）来理解用户-服务交互关系，预测精度仍会下降。本文针对上述挑战，提出一种基于多源协同特征的可扩展时间感知QoS预测策略TPMCF，实现高预测精度与快速响应。TPMCF通过利用用户-服务关系，结合图卷积与多头自注意力Transformer编码器提取的时空自编码特征，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方法。大量实验表明，TPMCF在预测精度上超越主流先进方法，同时确保高可扩展性与较快的响应速度。