摘要： 为更好地实践云计算为用户提供廉价按需服务的宗旨，满足服务请求者的个性化需求，提出一种面向个性化云服务的动态信任模型。基于细粒度服务思想定义个性化云服务，通过引入时间衰减因子和建立高效激励机制修正直接信任值，以灰色系统理论为基础计算实体间的评价相似度，并将评价相似度和推荐者的推荐可信度作为合成推荐信任值的重要因素，同时提出一种基于评价相似度的自信因子赋值方法，以提高合成综合信任值的准确性。实验结果表明，与GM-Trust模型及CCIDTM模型相比，该模型的交互成功率分别平均提高了4%和11%。

关键词: 个性化云服务, 激励机制, 推荐可信度, 评价相似度, 灰色系统理论, 自信调节因子

Abstract: In order to achieve cloud’s objective of providing low-cost and on-demand services for customers preferably, and to meet the customer’s personalized demands, a dynamic trust model for the personalized cloud services is proposed in this paper. It defines the personalized cloud service based on the fine-grained theory, and revises the direct trust with the time-decline coefficient and the incentive mechanism. On the other hand, it composes the recommendation trust value with the recommendation credibility and the evaluation similarity that is figured out based on the grey system theory. To improve the accuracy and scientific of trust evaluation, the method of dynamic setting self-confidence factor based on the evaluation similarity is designed. Experimental results show that the average transaction success rate can be increased by 4% and 11% compared with GM-Trust and CCIDTM model.

Key words: personalized cloud services, incentive mechanism, recommendation credibility, evaluation similarity, grey system theory, self-confidence adjustment factor

中图分类号: 

• TP309