We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.

中文翻译：

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基于层的复合信誉引导

我们为复合服务提出了一种新颖的通用信誉引导框架。在基于层的框架中考虑了多个与信誉相关的指标，以隐式反映组件服务的信誉。使用改进的随机森林算法可以了解组件服务的未来性能指标的重要性。我们提出了一种拓扑感知的森林深层神经网络（fDNN），以查找复合服务的信誉与组件服务的信誉指标之间的相关性。经过训练的fDNN模型以置信度值预测新组合服务的声誉。真实数据集的实验结果证明了该方法的有效性。