Dynamic recommendation trust model based on information entropy and heuristic rules in E-commerce environment

Under E-commence environment, because both parties of transaction lack the mutual basis of trust, transaction is facing the higher risk. We propose a dynamic recommendation trust model based on information entropy and heuristic rules. In the model, we take into account recommendation trustworthiness is related with acquaintance degree of between recommendation nodes and trust evaluator, and recommendation trustworthiness is also related with similarity of transaction contents at the same time. So we propose a novel computing method of concept similarity of transaction content based on trade goods ontology, in order to ensure objectivity and accuracy of computing, we adopt information entropy to avoid the defect of subjectivity of artificial weighted coefficient, and we adopt heuristic rules to resolve the problem that two concept similarity degree does not be distinguished while the number of information that two concept contains is the same. Based on experiments result analysis, slander and collaborative cheating of malicious nodes are restrained and held back by our model. In addition, experiment results show we propose concept similarity computing method is effective.