This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization
July-Sept. 2013 (vol. 6 no. 3)
pp. 289-299
Zibin Zheng, The Chinese University of Hong Kong , HK
Hao Ma, Microsoft Research, Redmond
Michael R. Lyu, The Chinese University of Hong Kong, Shatin
Irwin King, The Chinese University of Hong Kong, Shatin
With the increasing presence and adoption of web services on the World Wide Web, the demand of efficient web service quality evaluation approaches is becoming unprecedentedly strong. To avoid the expensive and time-consuming web service invocations, this paper proposes a collaborative quality-of-service (QoS) prediction approach for web services by taking advantages of the past web service usage experiences of service users. We first apply the concept of user-collaboration for the web service QoS information sharing. Then, based on the collected QoS data, a neighborhood-integrated approach is designed for personalized web service QoS value prediction. To validate our approach, large-scale real-world experiments are conducted, which include 1,974,675 web service invocations from 339 service users on 5,825 real-world web services. The comprehensive experimental studies show that our proposed approach achieves higher prediction accuracy than other approaches. The public release of our web service QoS data set provides valuable real-world data for future research.
Index Terms:
Web services,Quality of service,Accuracy,Collaboration,Predictive models,Throughput,matrix factorization,Web service,QoS prediction,user-collaboration
Citation:
Zibin Zheng, Hao Ma, Michael R. Lyu, Irwin King, "Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization," IEEE Transactions on Services Computing, vol. 6, no. 3, pp. 289-299, July-Sept. 2013, doi:10.1109/TSC.2011.59
Usage of this product signifies your acceptance of the Terms of Use.