复杂网络环境下基于推荐链分类的动态信任模型

针对网络环境中复杂的推荐信息处理问题，提出了一种基于推荐链分类的信任模型。该分类方法基于节点间的诚实属性，在实际经验数据的基础之上能选择出有效的推荐链。针对推荐信息的传播使用了以信息增益为基础的参数，使推荐信息更精准，考虑了时间的影响并且能把交互能力与诚实属性清楚地区分开。在最终的直接信任与推荐信息的聚合计算过程中采用了信息论中熵的概念，摆脱了以往主观设定参数的模糊性。模型中主要的聚合参数能随着交互的进行而不断地修正，达到了最贴近真实值的情形。仿真实验验证了新模型分类的有效性以及参数设置的合理性。

Dynamic trust model based on recommendation chain classification in complex network environment

According to the recommendation information processing problem in complex network environment, a trust model based on the recommendation chain classification was proposed. The classification method was based on honesty attribute of nodes, which could choose an effective recommendation chain on the basis of practical experience data. The recommendation information dissemination parameters were based on the information gain, which made recommendation information be more accurate. The factor of time was also considered in this model. The ability of interaction and the one of honesty were distinguished clearly. The concept of information entropy in information theory was used in the final aggregation calculation of direct trust and recommendation trust, which could get rid of the ambiguity of the previous subjective parameter settings. The main polymerization parameters could be continuously corrected with the interactions in order to achieve the situation being closest to the reality. Simulation results show the validity of recommendation chain classification and the rationality of the parameter settings in the proposed model.