Ngaffo Armielle Noulapeu, Ayeb Walid El, Choukair Zièd
Higher School of Communication of Tunis, Mediatron Laboratory, University of Carthage, Tunis, Tunisia.
Appl Intell (Dordr). 2022;52(1):1110-1125. doi: 10.1007/s10489-021-02478-0. Epub 2021 May 16.
The rise of high-quality cloud services has made service recommendation a crucial research question. Quality of Service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, perform a high-accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, we propose a data sparsity resilient service recommendation approach that aims to predict relevant services in a sustainable manner for end-users. Indeed, our method performs both a QoS prediction of the current time interval using a flexible matrix factorization technique and a QoS prediction of the future time interval using a time series forecasting method based on an AutoRegressive Integrated Moving Average (ARIMA) model. The service recommendation in our approach is based on a couple of criteria ensuring in a lasting way, the appropriateness of the services returned to the active user. The experiments are conducted on a real-world dataset and demonstrate the effectiveness of our method compared to the competing recommendation methods.
高质量云服务的兴起使服务推荐成为一个关键的研究问题。服务质量（QoS）被广泛用于表征用户调用服务的性能。为此，服务的QoS预测构成了一个决定性工具，使终端用户能够根据自身需求最佳地选择高质量的云服务。事实上，用户只使用了现有众多服务中的少数几种。因此，进行高精度的服务推荐成为一项具有挑战性的任务。为应对上述挑战，我们提出了一种抗数据稀疏性的服务推荐方法，旨在以可持续的方式为终端用户预测相关服务。实际上，我们的方法既使用灵活的矩阵分解技术对当前时间间隔进行QoS预测，又使用基于自回归积分移动平均（ARIMA）模型的时间序列预测方法对未来时间间隔进行QoS预测。我们方法中的服务推荐基于几个标准，以持久地确保返回给活跃用户的服务的适用性。实验在一个真实世界的数据集上进行，结果表明我们的方法与竞争推荐方法相比是有效的。
