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至关重要的研究问题。服务质量 (QoS) 被广泛用于表征用户调用的服务的性能。为此，服务的 QoS 预测构成了一个决定性的工具，使最终用户可以优化选择符合其需求的高质量云服务。事实是，用户只使用了广泛的现有服务中的一小部分。因此，执行高精度的服务推荐成为一项具有挑战性的任务。为了应对上述挑战，我们提出了一种数据稀疏弹性服务推荐方法，旨在以可持续的方式为最终用户预测相关服务。的确，我们的方法使用灵活的矩阵分解技术对当前时间间隔进行 QoS 预测，并使用基于自回归综合移动平均 (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，以持久的方式确保返回给活跃用户的服务的适当性。实验是在真实世界的数据集上进行的，并证明了我们的方法与竞争推荐方法相比的有效性。返回给活跃用户的服务的适当性。实验是在真实世界的数据集上进行的，并证明了我们的方法与竞争推荐方法相比的有效性。返回给活跃用户的服务的适当性。实验是在真实世界的数据集上进行的，并证明了我们的方法与竞争推荐方法相比的有效性。