QoS Value Prediction Using a Combination of Filtering Method and Neural Network Regression

  • Soumi ChattopadhyayEmail author
  • Ansuman Banerjee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11895)


With increasing demand and adoption of web services in the world wide web, selecting an appropriate web service for recommendation is becoming a challenging problem to address today. The Quality of Service (QoS) parameters, which essentially represent the performance of a web service, play a crucial role in web service selection. However, obtaining the exact value of a QoS parameter of service before its execution is impossible, due to the variation of the QoS parameter across time and users. Therefore, predicting the value of a QoS parameter has attracted significant research attention. In this paper, we consider the QoS prediction problem and propose a novel solution by leveraging the past information of service invocations. Our proposal, on one hand, is a combination of collaborative filtering and neural network-based regression model. Our filtering approach, on the other hand, is a coalition of the user-intensive and service-intensive models. In the first step of our approach, we generate a set of similar users on a set of similar services. We then employ a neural network-based regression module to predict the QoS value of a target service for a target user. The experiments are conducted on the WS-DREAM public benchmark dataset. Experimental results show the superiority of our method over state-of-the-art approaches.


  1. 1.
    Adamczak, R., et al.: Accurate prediction of solvent accessibility using neural networks-based regression. Proteins Struct. Funct. Bioinform. 56(4), 753–767 (2004)CrossRefGoogle Scholar
  2. 2.
    Amin, A., et al.: An approach to forecasting qos attributes of web services based on arima and garch models. In: ICWS, pp. 74–81. IEEE (2012)Google Scholar
  3. 3.
    Breese, J.S., et al.: Empirical analysis of predictive algorithms for collaborative filtering. In: Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  4. 4.
    Chattopadhyay, S., et al.: A framework for top service subscription recommendations for service assemblers. In: IEEE SCC, pp. 332–339 (2016)Google Scholar
  5. 5.
    Chen, X., et al.: Personalized qos-aware web service recommendation and visualization. IEEE TSC 6(1), 35–47 (2013)MathSciNetGoogle Scholar
  6. 6.
    Daniel, G.: Principles of Artificial Neural Networks, vol. 7. World Scientific, Singapore (2013) zbMATHGoogle Scholar
  7. 7.
    Demuth, H., Beale, M.: Neural Network Toolbox, vol. 4. The MathWorks Inc., Boston (2004)Google Scholar
  8. 8.
    Ester, M., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD, vol. 96, pp. 226–231 (1996)Google Scholar
  9. 9.
    Li, S., Wen, J., Luo, F., Ranzi, G.: Time-aware QoS prediction for cloud service recommendation based on matrix factorization. IEEE Access 6, 77716–77724 (2018)CrossRefGoogle Scholar
  10. 10.
    Li, S., et al.: From reputation perspective: a hybrid matrix factorization for QoS prediction in location-aware mobile service recommendation system. Mob. Inf. Syst. 2019, 8950508:1–8950508:12 (2019)Google Scholar
  11. 11.
    Lo, W., et al.: An extended matrix factorization approach for qos prediction in service selection. In: IEEE SCC, pp. 162–169. IEEE (2012)Google Scholar
  12. 12.
    Ma, Y., et al.: Predicting QoS values via multi-dimensional QoS data for web service recommendations. In: ICWS, pp. 249–256. IEEE (2015)Google Scholar
  13. 13.
    Qi, K., et al.: Personalized QoS prediction via matrix factorization integrated with neighborhood information. In: SCC, pp. 186–193. IEEE (2015)Google Scholar
  14. 14.
    Sarwar, B.M., et al.: Item-based collaborative filtering recommendation algorithms. WWW 1, 285–295 (2001)CrossRefGoogle Scholar
  15. 15.
    Shao, L., et al.: Personalized qos prediction for web services via collaborative filtering. In: IEEE ICWS, pp. 439–446 (2007)Google Scholar
  16. 16.
    Sun, H., et al.: Personalized web service recommendation via normal recovery collaborative filtering. IEEE TSC 6(4), 573–579 (2013)Google Scholar
  17. 17.
    Tang, M., et al.: Location-aware collaborative filtering for QoS-based service recommendation. In: ICWS, pp. 202–209. IEEE (2012)Google Scholar
  18. 18.
    Wang, S., et al.: Multi-dimensional QoS prediction for service recommendations. IEEE TSC 12, 47–57 (2016) Google Scholar
  19. 19.
    Wu, C., Qiu, W., et al.: Time-aware and sparsity-tolerant QoS prediction based on collaborative filtering. In: IEEE ICWS, pp. 637–640 (2016)Google Scholar
  20. 20.
    Wu, H., et al.: Collaborative QoS prediction with context-sensitive matrix factorization. Future Gener. Comp. Syst. 82, 669–678 (2018)CrossRefGoogle Scholar
  21. 21.
    Wu, X., et al.: Collaborative filtering service recommendation based on a novel similarity computation method. IEEE TSC 10(3), 352–365 (2017)Google Scholar
  22. 22.
    Zheng, Z., et al.: QoS-aware web service recommendation by collaborative filtering. IEEE TSC 4(2), 140–152 (2011)Google Scholar
  23. 23.
    Zheng, Z., et al.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE TSC 6(3), 289–299 (2013)Google Scholar
  24. 24.
    Zheng, Z., et al.: Investigating qos of real-world web services. IEEE TSC 7(1), 32–39 (2014)Google Scholar
  25. 25.
    Zou, G., Jiang, M., Niu, S., Wu, H., Pang, S., Gan, Y.: QoS-aware web service recommendation with reinforced collaborative filtering. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 430–445. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Information TechnologyGuwahatiIndia
  2. 2.Indian Statistical InstituteKolkataIndia

Personalised recommendations