  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.
