Skip to main content
Springer Nature Link
Log in

QoS Prediction in Dynamic Web Services with Asymmetric Correlation

  • Conference paper
  • First Online:
Algorithms and Architectures for Parallel Processing (ICA3PP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9528))

  • 1775 Accesses

Abstract

Web services are the mainstream implementation of service-oriented architectures in which both functional and non-functional Quality of Service (QoS) is significantly considered by service users and providers. Although multiple QoS-based models to QoS prediction for Web services via collaborative filtering have been proposed, the accuracy of existing ones is not adequate, since these works rarely have considered the influence of asymmetric correlation among service users on prediction accuracy. This paper combines asymmetric correlation among service users and asymmetric correlation propagation into the deviation computation of different service items in QoS prediction. In this paper, a novel method for QoS prediction in dynamic Web services is proposed, which includes a framework consisting of asymmetric correlation model, asymmetric correlation propagation model and deviation computation algorithm with correlation. To study the QoS prediction performance of our method, a well-known dataset consisting of about 1.97 million real-world QoS records is used in the experiments. The experimental results demonstrate that our method achieves better prediction accuracy than other well-known methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.wsdream.net.

References

  1. Zheng, Z., Michael, R.L.: Selecting an optimal fault tolerance strategy for reliable service-oriented systems with local and global constraints. J. IEEE Trans. Comput. 64, 219–232 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ikbel, G., Nawal, G., Tarak, C., Said, T., Mohamed, J.: Pruning based service selection approach under QoS and temporal constraints. In: 2014 IEEE International Conference on Web Services (ICWS), pp. 9–16. IEEE Computer Society, Washington, DC (2014)

    Google Scholar 

  3. Mustapha, A., Mohamed, Q., Zahi, J.: Leveraging formal concept analysis with topic correlation for service clustering and discovery. In: 2014 IEEE International Conference on Web Services (ICWS), pp. 153–160. IEEE Computer Society, Washington, DC (2014)

    Google Scholar 

  4. Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., Sheng, Q.Z.: Quality driven web services composition. In: WWW, pp. 411–421, (2003)

    Google Scholar 

  5. He, P., Zhu, J., Zheng, Z., Xu, J., Lyu, M.R.: Location-based hierarchical matrix factorization for web service recommendation. In: 2014 IEEE International Conference on Web Services, pp. 297–304. IEEE (2014)

    Google Scholar 

  6. Menasce, D.: Qos issues in web services. J. IEEE Internet Comput. 6, 72–75 (2002)

    Article  Google Scholar 

  7. Zheng, Z., Zhang, Y., Michael R, L.: Distributed QoS evaluation for real-world web services. In: 8th International Conference on Web Services (ICWS 2010), pp. 83–90. IEEE Computer Society, Washington, DC (2010)

    Google Scholar 

  8. Rong, W., Liu, K., Liang, L.: Personalized web service ranking via user group combining association rule. In: International Conference on Web Services, pp. 445–452. IEEE Computer Society, Washington, DC (2009)

    Google Scholar 

  9. Shao, L., Zhang, J., Wei, Y., Zhao, J., Xie, B., Mei, H.: Personalized QoS prediction for web services via collaborative filtering. In: International Conference on Web Services, pp. 439–446. IEEE Computer Society, Washington, DC (2007)

    Google Scholar 

  10. Zheng, Z., Ma, H., Michael, R.L., King, I.J.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4, 140–152 (2011)

    Article  Google Scholar 

  11. Zheng, Z., Ma, H., Michael R.L., King, I.: WSRec: a collaborative filtering based web service recommender system. In: 7th IEEE International Conference on Web Services, pp. 437–444. IEEE Computer Society, Washington, DC (2009)

    Google Scholar 

  12. Burke, R.: Hybrid recommender systems: survey and experiments. J. User Model. User-Adap. Interact. 12, 331–370 (2002)

    Article  MATH  Google Scholar 

  13. Hofmann, T.: Latent semantic models for collaborative filtering. J. Acm Trans. Inf. Syst. 22, 89–115 (2004)

    Article  Google Scholar 

  14. Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: SIAM Data Mining (SDM 2005), pp. 471–480 (2005)

    Google Scholar 

  15. Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: 10th International Conference on World Wide Web, pp. 285–295 (2001)

    Google Scholar 

  16. Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized QoS-aware web service recommendation and visualization. J. IEEE Trans. Serv. Comput. 6, 35–47 (2013)

    Article  Google Scholar 

  17. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Computer Supported Cooperative Work, pp. 175–186 (1994)

    Google Scholar 

  18. Breese, J. S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Uncertainty in Artificial Intelligence, pp. 43–52 (1998)

    Google Scholar 

  19. Jiang, Y., Liu, J., Tang, M., Liu X.: An effective web service recommendation method based on personalized collaborative filtering. In: 2011 IEEE International Conference on Web Services, pp. 211–218. IEEE Computer Society (2011)

    Google Scholar 

  20. Tang, M., Jiang, Y., Liu, J., Liu, X.: Location-aware collaborative filtering for qos-based service recommendation. In: 19th International Conference on Web Services (ICWS), pp. 202–209. IEEE (2012)

    Google Scholar 

  21. Gao, M., Wu, Z., Jiang, F.: Userrank for item-based collaborative filtering recommendation. J. Inf. Process. Lett. 111, 444–446 (2011)

    MathSciNet  MATH  Google Scholar 

  22. Gori, M., Pucci, A., Roma, V., Siena, I.: Itemrank: a random-walk based scoring algorithm for recommender engines. In: 20th International Joint Conference on Artificial Intelligence (IJCAI), pp. 778–781 (2007)

    Google Scholar 

  23. Zheng, Z., Michael R.L.: Component recommendation for cloud applications. In: 2nd International Workshop on Recommendation Systems for Software Engineering, pp. 323–345 (2010)

    Google Scholar 

  24. Esmaili, K.S., Neshati, M., Jamali, M., Abolhassani, H., Habibi, J.: Comparing performance of recommendation techniques in the blogsphere. In: ECAI 2006 Workshop on Recommender Systems, pp. 40–44 (2006)

    Google Scholar 

  25. Gao, M., Fu, Y., Chen, Y., Jiang, F.: User-weight model for item-based recommendation systems. J. Softw. 7, 2133–2140 (2012)

    Google Scholar 

  26. Liu, R., Jia, C., Zhou, T.: Personal recommendation via modified collaborative filtering. J. Phys. A Stat. Mech. Appl. 388, 462–468 (2009)

    Article  Google Scholar 

  27. Gong, M., Xu, Z., Xu, L.: Recommending web service based on user relationships and preferences. In: 20th International Conference on Web Services (ICWS), pp. 380–386. IEEE Computer Society, Washington, DC (2013)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 61502401 and No. 61379019, Guangdong Natural Science Foundation (Project No. 2014A030313151).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Southwest University for Nationalities, Chengdu, 610041, China

    Qi Xie & Mengtian Cui

  2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Bing Tang

  3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 510000, China

    Zibin Zheng

Authors
  1. Qi Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zibin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mengtian Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qi Xie .

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Guojun Wang

  2. The University of Sydney, Sydney, New South Wales, Australia

    Albert Zomaya

  3. University of Murcia, Murcia, Murcia, Spain

    Gregorio Martinez

  4. Hunan University , Changsha, China

    Kenli Li

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Xie, Q., Tang, B., Zheng, Z., Cui, M. (2015). QoS Prediction in Dynamic Web Services with Asymmetric Correlation. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9528. Springer, Cham. https://doi.org/10.1007/978-3-319-27119-4_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27119-4_31

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27118-7

  • Online ISBN: 978-3-319-27119-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics