Layer-based Composite Reputation Bootstrapping

Sajib Mistry

doi:10.1145/3448610

Layer-based Composite Reputation Bootstrapping

2021-09-14

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DOI：10.1145/3448610

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Abstract

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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.

Keywords:

Reputation bootstrapping

composite services

reputation indicators

composition topology

Random Forest

Deep Neural Network

bootstrapping confidence

Layer-based Composite Reputation Bootstrapping

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