With the fast-developing domain of cyber-physical systems (CPS), constructing the CPS with high-quality services becomes an imperative task. As one of the effective solutions for information overload in CPS construction, quality of service (QoS)-aware service recommendation has drawn much attention in academia and industry. However, the lack of most QoS values limits the recommendation performance and it is time-consuming for users to get the QoS values by invoking all the services. Therefore, a powerful prediction model is required to predict the unobserved QoS values. Considering the fact that most existing QoS prediction models are unable to effectively address the data-sparsity problem, a novel two-stage framework called AgQ is proposed for QoS prediction. Specifically, a data augmentation strategy is designed in the first stage to enlarge the training set by drawing additional virtual instances. In the second stage, a prediction model is applied that considers both virtual and factual instances during the training procedure. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the our QoS prediction framework and verify that the data augmentation strategy can indeed alleviate the data-sparsity problem. In terms of mean absolute error, taking the Multilayer Perceptron model as an example, the maximum improvement achieves 5% under 5% sparsity.

中文翻译：

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利用数据增强进行网络物理系统中的服务 QoS 预测

随着信息物理系统（CPS）领域的快速发展，构建具有高质量服务的CPS成为当务之急。作为CPS建设中信息过载的有效解决方案之一，服务质量（Quality of Service，QoS）感知服务推荐受到学术界和工业界的广泛关注。然而，大多数 QoS 值的缺乏限制了推荐性能，并且用户通过调用所有服务来获取 QoS 值是耗时的。因此，需要一个强大的预测模型来预测未观察到的 QoS 值。考虑到大多数现有的 QoS 预测模型无法有效解决数据稀疏问题，提出了一种称为 AgQ 的新型两阶段框架来进行 QoS 预测。具体来说，第一阶段设计了一种数据增强策略，通过绘制额外的虚拟实例来扩大训练集。在第二阶段，应用一个预测模型，该模型在训练过程中同时考虑虚拟和事实实例。我们对 WSDream 数据集进行了广泛的实验，以证明我们的 QoS 预测框架的有效性，并验证数据增强策略确实可以缓解数据稀疏问题。在平均绝对误差方面，以Multilayer Perceptron模型为例，在5%稀疏度下最大提升达到5%。我们对 WSDream 数据集进行了广泛的实验，以证明我们的 QoS 预测框架的有效性，并验证数据增强策略确实可以缓解数据稀疏问题。在平均绝对误差方面，以Multilayer Perceptron模型为例，在5%稀疏度下最大提升达到5%。我们对 WSDream 数据集进行了广泛的实验，以证明我们的 QoS 预测框架的有效性，并验证数据增强策略确实可以缓解数据稀疏问题。在平均绝对误差方面，以Multilayer Perceptron模型为例，在5%稀疏度下最大提升达到5%。