Earticle

초록

영어

With the increasing number of Web services, the goal of consumers becomes to discover and use services that lead to their experiencing the highest quality. Quality of Service (QoS) is important to evaluate the QoS performance of services to differentiate the qualities of service candidates. QoS is highly related to context information since service consumers are typically distributed in different geographical locations. Their experience is usually different. Invoking a huge number of Web services for consumers to predict the quality is time-consuming, resource- consuming, and sometimes even impractical. To address the challenge, this paper proposes a personalized context-aware recommendation approach for predicting the QoS of Web services and designs a prediction framework. This algorithm is a hybrid of the model-based and memory-based collaborative filtering algorithms. In our experiment, we collect QoS information from geographically distributed service consumers through the framework. Based on the QoS and context information, we predict the quality of services. As a result, we can obtain a list of recommended services for selection. Finally, the experiment shows that the algorithm using context information achieves better prediction.

목차

Abstract
 1. Introduction
 2. Personalized Context-aware Recommendation System
 3. Personalized Context-aware Prediction Approach
  3.1 Region Model Building
  3.2 QoS Prediction
 4. Experiment
  4.1 Data Collection
  4.2 Evaluation Metric
  4.3 Impact of Context
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhang Xue-Jie [ College of Computer and Information, Hohai University, Nanjing 210098, China, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ]
• Wang Zhi-Jian [ College of Computer and Information, Hohai University, Nanjing 210098, China ]
• Zhang Wei-Jiang [ College of Computer and Information, Hohai University, Nanjing 210098, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605