Advertisement

A Combination Approach to QoS Prediction of Web Services

  • Dongjin Yu
  • Mengmeng Wu
  • Yuyu Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7759)

Abstract

With a growing number of alternative Web services that provide the same functionality but differ in quality properties, the problem of selecting the best performing candidate service is becoming more and more important. However, users can hardly have invoked all services, meaning that the QoS values of some services are missing. In this paper, we propose a combination approach used to predict such missing QoS values. It employs an adjusted user-based algorithm using Pearson Correlation Coefficient to predict the QoS values of ordinary services. For services with constantly poor performance, however, it employs the average QoS values observed by different service users instead. An extensive performance study based on a real public dataset is finally reported to verify its effectiveness.

Keywords

Quality of Services Prediction Service Selection Web Services 

References

  1. 1.
    Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., Mei, H.: Personalized QoS prediction for web services via collaborative filtering. In: IEEE International Conference on Web Services, Salt Lake City, USA, pp. 439–446 (2007)Google Scholar
  2. 2.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: Qos-aware web service recommendation by collaborative filtering. IEEE Transactions on Service Computing 4(2), 140–152 (2011)CrossRefGoogle Scholar
  3. 3.
    Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized QoS-aware web service recommendation and visualization. IEEE Transactions on Services Computing PP(99) (2011)Google Scholar
  4. 4.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: 14th Conference on Uncertainty in Artificial Intelligence, Madison, USA, pp. 43–52 (1998)Google Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: ACM SIGIR Conference, Berkeley, USA, pp. 230–237 (1999)Google Scholar
  6. 6.
    Jin, R., Chai, J.Y., Si, L.: An automatic weighting scheme for collaborative filtering. In: ACM SIGIR Conference, Sheffield, UK, pp. 337–344 (2004)Google Scholar
  7. 7.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transaction on Information System 22(1), 143–177 (2004)CrossRefGoogle Scholar
  8. 8.
    Sarwar, B., Karypic, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International World Wide Web Conference, Hong Kong, China, pp. 285–295 (2001)Google Scholar
  9. 9.
    Wang, J., Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: ACM SIGIR Conference, Seattle, USA, pp. 501–508 (2006)Google Scholar
  10. 10.
    Si, L., Jin, R.: Flexible mixture model for collaborative filtering. In: International Conference on Machine Learning, Washington, USA (2003)Google Scholar
  11. 11.
    Hofmann, T.: Collaborative filtering via Gaussian probabilistic latent semantic analysis. In: ACM SIGIR Conference, Toronto, Canada, pp. 259–266 (2003)Google Scholar
  12. 12.
    Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transaction on Information System 22(1), 89–115 (2004)CrossRefGoogle Scholar
  13. 13.
    Zheng, Z., Zhang, Y., Lyu, M.R.: Distributed QoS evaluation for real-world web services. In: International Conference on Web Services, Miami, USA, pp. 83–90 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dongjin Yu
    • 1
  • Mengmeng Wu
    • 1
  • Yuyu Yin
    • 1
  1. 1.College of ComputerHangzhou Dianzi UniversityHangzhouChina

Personalised recommendations