面向可信和行为模式匹配的两层服务选择方法

Trust and behavioral modeling based two layer service selection

在面向服务框架下,用户对服务有着不同的交互经验,并根据这些经验建立信任且对其评价,然而用户可能产生错误的评价进而影响公正的选择.为解决这一问题,提出一种两层选择算法选取可信并且符合用户偏好的服务.第1层用户将自己的经验与群体的评价趋势进行比对,优化自身的记录,生成更加可靠的经验信息.第2层用户由自己的偏好选出最符合需求的建议者.结果表明,这个方案可以过滤掉恶意的或经验不足的评价者,并选择出符合用户行为模式的服务.

In the Service-Oriented Architecture（SOA）,the consumer has many experiences in service,and then based on the experiences he builds trust and reputation.But lack of experiences or a malicious adviser will lead to unfair selection.In this paper,we propose a two-layer selecting algorithm to meet the demand of the consumer.In the first layer the consumer uses social records update his own to get more accurate experience.Afterwards,in the second layer,the consumer selects the most suitable.The result shows that our approach will filter the malicious participants,and then chose the services which fit consumer’s preference.