基于朴素贝叶斯的信任决策模型

Trust Decision Model Based on Naive Bayesian

信任关系研究一直是解决人类复杂社会问题的有效手段.当前信任研究注重信任度的量化分析,忽视了信任对于行为决策的复杂作用.针对信任具有模糊性的特点,综合考虑信任关系中诸多要素,提出了一种新的信任决策模型.该模型将朴素贝叶斯算法和模糊理论相结合,以直接信任、间接信任、信誉作为信任的特征属性,根据朴素贝叶斯理论利用先验条件概率计算获得信任交互结果的后验概率.模型将凭借交互结果的后验概率帮助信任主体进行决策分析.实验表明,该模型具有较高的准确性,而且对恶意推荐有一定的抵御功能.

Trust research has effective means on solving the problem of complex human society.At present,trust research focus on trust metric,but ignore the important role of trust for decision making.Aimed to the fuzzy of trust,and consider trust relationship among various factors,this paper proposes a new trust model.This model combines the Naive Bayesian algorithm and fuzzy theory.It makes direct trust,indirect trust and reputation as trust characteristic attributes.According to Naive Bayesian theory,the prior condition probability is used to calculate the posterior probability of trust interactive results.This model will help truster to make decision analysis by the result of interaction.Experiments show that the model not only has high accuracy,but also has function to against malicious recommendation.