中南大学学报（自然科学版）Issue(1)：82-90,9.DOI:10.11817/j.issn.1672-7207.2016.01.013
基于混合推荐和隐马尔科夫模型的服务推荐方法
Recommending services via hybrid recommendation algorithms and hidden Markov model in cloud
摘要
Abstract
With the increase of the number of users using web services for online activities through thousands of services, proper services must be obtained; however, the existing methods such as content-based approaches or collaborative filtering do not consider new users and redundant services. An effective approach was proposed to recommend the most appropriate services in a cloud computing environment. Firstly, a hybrid collaborative filtering method was proposed to recommend services. The method greatly enhances the prediction of the current QoS value which may differ from that of the service publication phase. Secondly, a strategy was proposed to obtain the preferences of the new users who are neglected in other research. Thirdly, a HMM (hidden Markov model)-based approach was proposed to identify redundant services in a dynamic situation. Finally, several experiments were set up based on real data set and publicly published web services data set. The results show that the proposed algorithm has better performance than other methods.关键词
协同过滤/服务选择/新用户学习/隐马尔科夫模型/冗余检测Key words
collaborative filtering/service selection/new user learning/hidden Markov model/redundancy detection分类
信息技术与安全科学引用本文复制引用
马建威,陈洪辉,STEPHAN Reiff-Marganiec..基于混合推荐和隐马尔科夫模型的服务推荐方法[J].中南大学学报（自然科学版）,2016,(1):82-90,9.基金项目
国家自然科学基金资助项目(71071160)；全军后勤科研重点计划项目(BWS14J032)；湖南省优秀研究生创新项目(CX2011B024)；国防科技大学优秀研究生创新项目(B110502)；第三军医大学人文社科基金资助项目(2015XRW10)(Projects(61070216,71071160) supported by the National Natural Science Foundation of China （71071160）
Project(BWS14J032) supported by the Logistics Research Plan of Chinese PLA （BWS14J032）
Project(CX2011B024) supported by the Outstanding Graduate Student Innovation Fund of Hunan Province （CX2011B024）
Project(B110502) supported by the Outstanding Graduate Student Innovation Fund of National University of Defense Technology （B110502）
Project(2015XRW10) supported by Social Science Foundation of Third Military Medical University) （2015XRW10）
