[1]江晓苏,魏延,邱炳发. QoS感知的Web服务个性化推荐[J].计算机技术与发展,2015,25(12):85-90.
　JIANG Xiao-su,WEI Yan,QIU Bing-fa. QoS-aware Web Services Personalized Recommendation[J].,2015,25(12):85-90.
点击复制

 QoS感知的Web服务个性化推荐()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年12期
页码:
85-90
栏目:
智能、算法、系统工程
出版日期:
2015-12-10

文章信息/Info

Title:
 QoS-aware Web Services Personalized Recommendation
文章编号:
1673-629X（2015)12-0085-06
作者:
 江晓苏; 魏延; 邱炳发
 重庆师范大学 计算机与信息科学学院
Author(s):
 JIANG Xiao-su; WEI Yan; QIU Bing-fa
关键词:
 ; Web服务推荐; QoS感知; 用户偏好; 支持向量机
Keywords:
 ; Web services recommendation; QoS-awareness; user preferences; support vector machine
分类号:
TP393
文献标志码:
A
摘要:
 服务质量( Quality of Service,QoS)是Web服务推荐中的关键问题之一. 文中提出了一种基于QoS感知的Web服务推荐模型,该模型通过Web服务的历史QoS信息训练支持向量机( SVM)得到一个服务选择函数,通过此服务选择函数匹配当前用户的服务需求,进而得到一个服务候选集,通过这种支持向量机分类方法使服务的发现和选择具有机器学习的能力. 最后结合用户赋予QoS属性的权重偏好进行服务排序,为用户推荐最适合的服务. 通过实验仿真,验证了该服务分类方法的可行性和有效性.
Abstract:
 QoS( Quality of Service,QoS) is one of the key issues in Web services recommended. A Web services recommendation model based on QoS-awareness was proposed in this paper. This model trains Support Vector Machine ( SVM) to get a service selection func-tion through Web services history QoS information,by the use of the service selection function matching the current user’’s demand for services,then get a service candidate set,through the SVM classification method makes the services discovery and selection has the ability of machine learning. Finally,combined with the user given QoS attributes weights,order all services and recommend the most appropriate service for users. The experimental simulation shows that this service classification method is feasible and effective.

相似文献/References:

[1]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(12):1.
[2]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(12):5.
[3]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(12):13.
[4]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(12):21.
[5]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(12):25.
[6]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(12):29.
[7]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(12):34.
[8]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(12):38.
[9]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(12):43.
[10]余松平[][],蔡志平[],吴建进[],等. GSM-R信令监测选择录音系统设计与实现[J].计算机技术与发展,2014,24(07):47.
　YU Song-ping[][],CAI Zhi-ping[] WU Jian-jin[],GU Feng-zhi[]. Design and Implementation of an Optional Voice Recording System Based on GSM-R Signaling Monitoring[J].,2014,24(12):47.

更新日期/Last Update: 2016-01-29