针对移动Agent 电子商务环境,大多数基于声誉的信任算法是单维度的,评价的节点只对历史交易给出单一的评价,并不区分节点服务内容,给出的信任评价粒度较粗.对此,提出了一种基于声誉的多维度信任算法RMDT(reputation-based multi-dimensional trust).给出了一种新的推荐可信度计算方法,并运用自信因子综合直接信任和推荐信任来对网络内的节点进行信任评估.定义的时间敏感函数使RMDT 具有一定的奖惩机制,实现了信任的动态衰减.通过将交易评价体系和权重体系引入多维度机制,RMDT 较好地体现了个体偏好、风险态度等主观因素对信任计算的影响,增强了信任算法在交易单个属性上的敏感性.

For the mobile-agent-based e-commerce environment, most reputation-based trust algorithms are onedimensional. They are only based on the node’s historical transactions, and the services are not taken into account, so the evaluations are coarse-gained. This paper proposes a reputation-based multi-dimensional trust (RMDT) algorithm which makes use of a self-confident coefficient to synthesize the directed and the reference trustworthiness to evaluate the node in the network. The time sensitive function in this paper not only serves as a reward and punishment mechanism, but is also dynamically attenuate in its trustworthiness. Both the transaction evaluation system and the weight system are introduced in the multi-dimensional trust mechanism. RMDT can uncover the influence on trust computation caused by the subjective factors, such as individual predilection and risk attitude. In addition, the sensitivity of RMDT on the single attribute is greatly improved.

国家自然科学基金(70672041); 湖北省自然科学基金(2007ABA307); 中央高校基本科研业务费专项基金(2010MS112)