A Fine-Grain Trust Model Based on Domain and Bayesian Network for P2P E-Commerce System

Trust model has been widely applied to the field of P2P e-commerce, but the trust grain of the current trust model of P2P e-commerce is too rough to embody the peers real behavior very well. In order to solve the problem, a novel fine-grain trust model (FG-trust) is presented, which can complete the trust computation of one peer in different domains and different aspects. The model adopts the technology of multi-agent, uses the agent to manage and maintain the network and divides the network into multiple domains. Each domain sets a domain-agent to manage the peers in it and a manager-agent is set in the entire network to manage the messaging between inter-domains. In order to measure the trust value of one peer in any domain and take full account of the impact of different relationships between domains on recommendation trust, the concept of domain-model is introduced into FG-trust. The model uses Bayesian network to compute the peers trust value, which can predict the trust value of one peer in any aspects. And an updating method of Bayesian network model and domain-model is proposed, which can constrain the peers malicious behavior effectively. Finally, the effectiveness and feasibility of this model are illustrated by the experiments.