With the rapid development of Web services, how to identify services with high Quality of Service (QoS) becomes a hot research topic. Since time-series QoS records are highly nonlinear, complex and uncertain, it is difficult to make accurate predictions through conventional mathematic methods. In order to deal with the challenging issue, this paper proposes a novel personalized QoS prediction approach considering both the temporal dynamics of QoS attributes and the influence of different QoS records. First, slide-window based data grouping is firstly utilized to obtain training dataset for regression model. Then we take the different influence of history QoS records at different time into consideration and eventually propose a weighted-SVM model for QoS prediction. Compared to Auto-Regressive Moving Average Model (ARMA), standard SVM and Collaborative Filtering (CF), the proposed approach in the paper can improve significantly the accuracy in personalized QoS prediction.


Web service QoS prediction Temporal dynamics Support vector machine 



The research is supported by “Natural Science Foundation of Hunan Province” (No.2016JJ3154), “National Natural Science Foundation of China” (No.61202095), “Scientific Research Project for Professors in Central South University, China” (No. 904010001), and “Innovation Project for Graduate Students in Central South University” (No. 502210017).


  1. 1.
    Haner, M., Hinton, D., Klein, T.E.: Method of dynamically adjusting quality of service (QoS) targets: US, US 20060212594 A1[P] (2006)Google Scholar
  2. 2.
    Alrifai, M., Risse, T.: Combining global optimization with local selection for efficient QoS-aware service composition. In: Proceedings of the 18th International Conference on World Wide Web (2009)Google Scholar
  3. 3.
    Zheng, Z., Ma, H., Lyu, M.R., et al.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)CrossRefGoogle Scholar
  4. 4.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. Nips 1(1), 1257–1264 (2012)Google Scholar
  5. 5.
    Fouss, F., Pirotte, A., Renders, J.M., et al.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)CrossRefGoogle Scholar
  6. 6.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings 14th Conference on Uncertainty Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  7. 7.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRefGoogle Scholar
  8. 8.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: WSRec: A collaborative filtering based Web service recommender system. In: ICWS, pp. 437–444 (2009)Google Scholar
  9. 9.
    Zheng, Z., Ma, H., Lyu, M.R., King, I.: QoS-aware Web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)CrossRefGoogle Scholar
  10. 10.
    Godse, M., Bellur, U., Sonar, R.: Automating QoS based service selection. In: ICWS, pp. 534–541 (2010)Google Scholar
  11. 11.
    Amin, A., Colman, A., Grunske, L.: An approach to forecasting QoS attributes of web services based on ARIMA and GARCH models. In: ICWS, pp. 74–81 (2012)Google Scholar
  12. 12.
    Zhang, J., Song, J.: A short-term prediction in web service QoS based on RBF neural network. J. Liaoning Eng. Technol. Univ. 29(5), 918–921 (2010)Google Scholar
  13. 13.
    Zheng, X., Zhao, J., Cheng, Z., Xie, B.: A WebService response time dynamic prediction method. In: Mini-micro Systems, vol. 8 (2011)Google Scholar
  14. 14.
    Yang, J.F., Zhai, Y.J., Wang, D.F., et al.: Time series prediction based on support vector regression. Proc. CSEE 25(17), 110–114 (2005)Google Scholar
  15. 15.
    Zhang, Y., Zheng, Z., Lyu, M.R.: WSPred: A time-aware personalized QoS prediction framework for web services. In: Proceedings of the 22th IEEE Symposium on Software Reliability Engineering (ISSRE 2011) (2011)Google Scholar
  16. 16.
    Kurogi, S., Koyama, R., Tanaka, S., et al.: Forecasting using first-order difference of time series and bagging of competitive associative nets. In: 2007 International Joint Conference on Neural Networks, IJCNN 2007, pp. 166–171. IEEE (2007)Google Scholar
  17. 17.
    Hsu, B.C., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification (2012)Google Scholar
  18. 18.
    Box, G.E., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1976). revised ed.zbMATHGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.School of SoftwareCentral South University ChangshaChangshaChina

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