Layer-based Composite Reputation Bootstrapping
This work addresses reputation bootstrapping for composite services, which is an incremental improvement in service-oriented computing.
The paper tackles the problem of predicting reputation for composite services by learning from multiple reputation indicators of component services using a topology-aware Forest Deep Neural Network, achieving efficiency as proven by experimental results on a real-world dataset.
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.