Cluster Computing

, Volume 22, Supplement 6, pp 14231–14240 | Cite as

A design of intelligent QoS aware web service recommendation system

  • N. AnithadeviEmail author
  • M. Sundarambal


In web services environment, making an appropriate service recommendation to improve the web service composition ability and decrease composition cognition is a promising approach. Web service filtering is one of the essential technique for recommending QoS based web services. Collaborative Filtering (CF) plays major role in Web service recommendation which intends to predict missing QoS values of web services. QoS prediction techniques, emphasize more on user’s personalized influence of services and service QoS factors such as response time and throughput. The proposed model merges the location preference and personalized usage influences of the requester with Privacy, Demand, Satisfaction and Retention level assessment. Neuro Fuzzy Logic (NFL) is introduced to improve classification accuracy to intelligently identify the untrustworthy Web services. Intelligent Neuro Fuzzy Collaborative filtering (INFCF) for QoS aware web service recommendation is designed to enhance the QoS in web service recommendation.


Neuro Fuzzy Logic (NFL) Collaborative Filtering (CF) Qulaity of Service (QoS) Intelligent Neuro Fuzzy Collaborative filtering (INFCF) 


  1. 1.
    Lü, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)CrossRefGoogle Scholar
  2. 2.
    Shani, G., Gunawardana, A.: Evaluating recommendation systems, In: Recommender Systems Handbook, pp. 257–297, Springer, New York (2011)Google Scholar
  3. 3.
    Chen, Xi, et al.: Personalized qos-aware web service recommendation and visualization. IEEE Trans. Serv. Comput. 6(1), 35–47 (2013)CrossRefGoogle Scholar
  4. 4.
    Zheng, Z., et al.: Qos-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4(2), 140–152 (2011)CrossRefGoogle Scholar
  5. 5.
    Yao, L., et al.: Recommending web services via combining collaborative filtering with content-based features. In: Web Services (ICWS), 2013 IEEE 20th International Conference on IEEE (2013)Google Scholar
  6. 6.
    Tang, M., et al.: Location-aware collaborative filtering for QoS-based service recommendation. Web Services (ICWS), 2012 IEEE 19th International Conference on IEEE (2012)Google Scholar
  7. 7.
    Zheng, Z., Zhang, Y., Lyu, M.R.: Distributed qos evaluation for real-world web services. In: Web Services (ICWS), 2010 IEEE International Conference on IEEE (2010)Google Scholar
  8. 8.
    Deng, S.G., et al.: Trust-based personalized service recommendation: a network perspective. J. Comput. Sci. Technol. 29(1), 69–80 (2014)CrossRefGoogle Scholar
  9. 9.
    Feddaoui, I., et al.: A Survey on web service mining using QoS and recommendation based on multidimensional approach. In: Intelligent Interactive Multimedia Systems and Services 2016, pp. 439–450. Springer, Cham (2016)Google Scholar
  10. 10.
    Ludwig, H.: Web services qos: external slas and internal policiesor: how do we deliver what we promise? In: Proceedings of the Fourth International Conference on Web Information Systems Engineering Workshops, pp. 115–120 (2003)Google Scholar
  11. 11.
    Melville, P., Sindhwani, V: Recommender systems, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598Google Scholar
  12. 12.
    Lee, Danielle: Personalized Recommendations Based on Users’information-Centered Social Networks. University of Pittsburgh, Diss (2013)Google Scholar
  13. 13.
    Nelles, O., Fink, A., Babuka, R., Setnes, M.: Com- parison of two construction algorithms for Takagi- Sugeno fuzzy models. Int. J. Appl. Math. Comput. Sci. 10(4), 835–855 (2000)zbMATHGoogle Scholar
  14. 14.
    Werbos, P.: The Toots of the Back Propagation: From Ordered Derivatives to Neural Networks and Political Forecasting. Wiley, New York (1993)Google Scholar
  15. 15.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content boosted collaborative filtering for improved recommendations. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp. 187–192, Edmonton, Alberta (2002)Google Scholar
  16. 16.
    Mooney, R.J., Roy, L.:Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conferenceon Digital Libraries, pp. 195–204, San Antonio (2000)Google Scholar
  17. 17.
    Pazzani, Michael J., Billsus, Daniel: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)CrossRefGoogle Scholar
  18. 18.
    Mehdi, M., Bouguila, N., Bentahar, J.: Trust and reputation of web services through qos correlation lens. IEEE Trans. Serv. Comput. 9, 1 (2015)Google Scholar
  19. 19.
    Zhang, J.: Trustworthy web services: actions for now. ITProfessional 7(1), 32–36 (2005)Google Scholar
  20. 20.
    Hang, C.W., Singh, M.P.: Trustworthy service selection and composition. ACM Trans. Auton. Adapt. Syst. 6(1), 1–17 (2011)CrossRefGoogle Scholar
  21. 21.
    Farag, W.A., Quintana, V.H., Lambert-Torres, G.: A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems. IEEE Trans. Neural Netw. 9(5), 756–767 (1998)CrossRefGoogle Scholar
  22. 22.
    Tahmasebi, P., Hezarkhani, A.: A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput. Geosci. 42, 18–27 (2012)CrossRefGoogle Scholar
  23. 23.
    Nguyen, H.T., Zhao, W., Yang, J.: A trust and reputation model based on bayesian network for web services. In: 2010 IEEE International Conference on Web Services (ICWS). IEEE (2010)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science and Engineering/Information TechnologyCoimbatore Institute of TechnologyCoimbatoreIndia
  2. 2.Electrical and Electronics Engineering DepartmentCoimbatore Institute of TechnologyCoimbatoreIndia

Personalised recommendations