ULMF: Web Service QoS Collaborative Prediction with Explicit Ratings and Implicit User Location

摘要：

　　Since more and more Web services with equivalent function but different QoS are available in Internet,predicting unknown QoS value is often required for Web service selection and composition.Previous prediction approaches underestimate the role of user location information,which have a significant impact on user QoS experience according to our empirical analysis on public real-world QoS dataset-WSDream.In this paper,we proposed a personalized Web service QoS collaborative prediction method,which extends matrix factorization model by smoothly incorporating both explicit QoS values user rated in the past and implicit user location information that inherently existed in rating-oriented model.Experimental results show that compared with other approaches,suggested method in this paper can achieve higher prediction accuracy and as well as performs well in cold start situation.

关键词： Web service QoS collaborative prediction matrix factorization user location

作者: Limin Shen Zhen Chen Feng Li

作者单位: College of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China;The Key L College of Computer and Communication Engineering,Northeastern University at Qinhuangdao,Qinhuangdao

会议类型: 国内会议

会议名称: 第10届全国计算机支持的协同工作学术会议暨中国计算机学会协同计算专委年度工作会议

会议地点: 太原

会议语种:英文

页码: 494-501

在线出版日期: 2015-08-28（万方平台首次上网日期，不代表论文的发表时间）