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由于Web Services数量的快速增加以及用户偏好的不同,在环绕智能环境下自适应地为用户选择合适服务是比较困难的.论文提出一种利用对服务的评价信息来获取用户偏好的学习机制.在此基础上,论文给出了基于信誉度和多属性决策的动态自适应服务选择算法.该算法首先利用学习到的偏好信息来产生当前用户的服务请求偏好值,然后利用加权欧氏距离及信誉度机制来选择最合适的服务推荐给用户.最后通过原型系统测试验证了算法的有效性和可用性.
Due to the rapid increase of the number of Web Services and the different preferences of users, it is difficult to adaptively select the appropriate service for users in Surrounding Intelligent Environment.This paper presents a learning mechanism that uses the evaluation information of services to obtain user preferences. Based on this, the paper presents a dynamic adaptive service selection algorithm based on reputation and multi-attribute decision-making, which first generates the current user service preference value using the learned preference information, and then uses weighted Euclidean distance and reputation Degree mechanism to choose the most suitable service to recommend to the user.Finally, the prototype system test verifies the effectiveness and usability of the algorithm.