Applications of Cloud Services are increasing day by day, and so is the difficulty of choosing the best-suited service for a customer. Quality of Service (QoS) parameters can be used for quality assurance and evaluation; further, a service can be recommended based on these QoS parameters’ values. Recommendation systems are getting much attention lately. It has a crucial role in almost all the major commercial platforms and many improvements are being made to make the recommendations more precise and closer to the user’s requirements. Conventional Machine Learning algorithms and statistical analysis methods, presently are not that efficient in learning the complex correlation between data elements. Lately, Deep Learning models have proven to be practical and precise in areas like natural language processing, image processing, data mining, & data interpretation. However, there are not many examples of complete Deep Learning applications for cloud service recommendation systems, though some works partially use Deep Learning. We propose the Ensemble of Deep Neural Networks (EDNN) method, which is of the hybrid type, i.e., the fusion of neighborhood-based and neural network model-based methods. The output obtained from both the models are combined using another different neural network model. Our approach for predicting QoS values is simple and different from previous works, and the results show that it outperforms other classical methods marginally.

云服务的应用日益增多，为客户选择最适合的服务的难度也越来越大。服务质量 (QoS) 参数可用于质量保证和评估；此外，可以根据这些 QoS 参数的值推荐服务。推荐系统最近备受关注。它在几乎所有主要商业平台中都发挥着至关重要的作用，并且正在进行许多改进以使推荐更精确、更贴近用户的要求。传统的机器学习算法和统计分析方法目前在学习数据元素之间的复杂相关性方面效率不高。最近，深度学习模型已被证明在自然语言处理、图像处理、数据挖掘和 数据解读。然而，虽然有些作品部分使用了深度学习，但针对云服务推荐系统的完整深度学习应用的例子并不多。我们提出了深度神经网络集成（EDNN）方法，它是混合类型的，即基于邻域和基于神经网络模型的方法的融合。使用另一个不同的神经网络模型组合从这两个模型获得的输出。我们预测 QoS 值的方法很简单，与以前的工作不同，结果表明它略微优于其他经典方法。我们提出了深度神经网络集成（EDNN）方法，它是混合类型的，即基于邻域和基于神经网络模型的方法的融合。使用另一个不同的神经网络模型组合从这两个模型获得的输出。我们预测 QoS 值的方法很简单，与以前的工作不同，结果表明它略微优于其他经典方法。我们提出了深度神经网络集成（EDNN）方法，它是混合类型的，即基于邻域和基于神经网络模型的方法的融合。使用另一个不同的神经网络模型组合从这两个模型获得的输出。我们预测 QoS 值的方法很简单，与以前的工作不同，结果表明它略微优于其他经典方法。