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Layer-based Composite Reputation Bootstrapping
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AI
DOI：10.1145/3448610.png)
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摘要
En 中文
We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputationrelated 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.
Keyword:
Reputation bootstrapping
composite services
reputation indicators
composition topology
Random Forest
Deep Neural Network
bootstrapping confidence
期刊
IF:
4.1
论文数:
896
被引数:
1.9K
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