Skip to main content
SpringerLink
Log in

QoS-Based Concurrent User-Service Grouping for Web Service Recommendation

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Recently, tremendous growth of web services to share the program, data and resources requires the optimal recommendation strategy. The major issues observed in existing recommendation strategies are scalability, sparsity and the cold start. The employment of matrix factorization (MF) models addressed all the issues effectively. But, they increase the scalability of the system. This paper proposes the new framework that contains web service grouping, distance estimation, service utilization level estimation and the item-to-item comparison (Pearson Correlation Coefficient (PCC)) to improve the recommendation performance. The grouping of users according to the Haversine distance formulation to reduce the complexity in the relevant web service recommendation against the complex queries. The locations and the fields in the services utilization in proposed work provide the effective recommendation performance. The comparative analysis between the proposed novel recommendation framework with the existing techniques assures the effectiveness of proposed approach in web service recommendation.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zheng, Z., Zhang, Y., and Lyu, M.R., Investigating QoS of real-world web services, IEEE Trans. Serv. Comput., 2014, vol. 7, pp. 32–39.

    Article  Google Scholar 

  2. Chen, X., Zheng, Z., Yu, Q., and Lyu, M.R., Web service recommendation via exploiting location and QoS information, IEEE Trans. Parallel Distrib. Syst., 2014, vol. 25, pp. 1913–1924.

    Article  Google Scholar 

  3. Chen, X., Zheng, Z., Liu, X., Huang, Z., and Sun, H., Personalized QoS-aware web service recommendation and visualization, IEEE Trans. Serv. Comput., 2013, vol. 6, pp. 35–47.

    Article  Google Scholar 

  4. Klein, A., Ishikawa, F., and Honiden, S., Towards network-aware service composition in the cloud, Proceedings of the 21st International Conference on World Wide Web, 2012, pp. 959–968.

    Chapter  Google Scholar 

  5. Costante, E., Paci, F., and Zannone, N., Privacy-aware web service composition and ranking, Web Services (ICWS), 2013 IEEE 20th International Conference on, 2013, pp. 131–138.

    Chapter  Google Scholar 

  6. Kang, G., Tang, M., Liu, J., Liu, F., and Cao, B., Diversifying web service recommendation results via exploring service usage history, IEEE Trans. Serv. Comput., 2015, vol. 9, no. 4, pp. 566–579.

    Article  Google Scholar 

  7. Yu, C. and Huang, L., A Web service QoS prediction approach based on time-and location-aware collaborative filtering, Serv. Oriented Comput. Appl., 2016, vol. 10, pp. 135–149.

    Article  Google Scholar 

  8. Zhu, J., He, P., Zheng, Z., and Lyu, M.R., A privacy-preserving QoS prediction framework for web service recommendation, IEEE International Conference on Web Services (ICWS), 2015, pp. 241–248.

    Google Scholar 

  9. Sheng, Q.Z., Qiao, X., Vasilakos, A.V., Szabo, C., Bourne, S., and Xu, X., Web services composition: A decade’s overview, Inf. Sci., 2014, vol. 280, pp. 218–238.

    Article  Google Scholar 

  10. Zheng, Z., Ma, H., Lyu, M.R., and King, I., QoS-aware web service recommendation by collaborative filtering, IEEE Trans. Serv. Comput., 2011, vol. 4, pp. 140–152.

    Article  Google Scholar 

  11. Zheng, Z., Ma, H., Lyu, M.R., and King, I., QoS-aware web service recommendation by collaborative filtering, IEEE Trans. Serv. Comput., 2011, vol. 4, pp. 140–152.

    Article  Google Scholar 

  12. Zheng, Z., Ma, H., Lyu, M.R., and King, I., Collaborative web service QoS prediction via neighborhood integrated matrix factorization, IEEE Trans. Serv. Comput., 2013, vol. 6, pp. 289–299.

    Article  Google Scholar 

  13. Wu, J., Chen, L., Zheng, Z., Lyu, M.R., and Wu, Z., Clustering web services to facilitate service discovery, Knowl. Inf. Syst., 2014, vol. 38, pp. 207–229.

    Article  Google Scholar 

  14. Fan, X., Hu, Y., Li, J., and Wang, C., Context-aware ubiquitous web services recommendation based on user location update, 2015 International Conference on Cloud Computing and Big Data (CCBD), 2015, pp. 111–118.

    Chapter  Google Scholar 

  15. Lo, W., Yin, J., Deng, S., Li, Y., and Wu, Z., An extended matrix factorization approach for QoS prediction in service selection, IEEE Ninth International Conference on Services Computing (SCC), 2012, pp. 162–169.

    Google Scholar 

  16. Yin, H., Cui, B., Chen, L., Hu, Z., and Zhang, C., Modeling location-based user rating profiles for personalized recommendation, ACM Trans. Knowl. Discovery Data, 2015, vol. 9, p.19.

    Google Scholar 

  17. Hoens, T.R., Blanton, M., Steele, A., and Chawla, N.V., Reliable medical recommendation systems with patient privacy, ACM Trans. Intell. Syst. Technol., 2013, vol. 4, p.67.

    Article  Google Scholar 

  18. Wu, J., Chen, L., Feng, Y., Zheng, Z., Zhou, M.C., and Wu, Z., Predicting quality of service for selection by neighborhood-based collaborative filtering, IEEE Trans. Syst. Man Cybern.: Syst., 2013, vol. 43, pp. 428–439.

    Article  Google Scholar 

  19. Yu, D., Liu, Y., Xu, Y., and Yin, Y., Personalized QoS prediction for web services using latent factor models, Services Computing (SCC), 2014 IEEE International Conference on, 2014, pp. 107–114.

    Google Scholar 

  20. Zheng, H., Yang, J., Zhao, W., and Bouguettaya, A., QoS analysis for web service compositions based on probabilistic QoS, International Conference on Service-Oriented Computing, 2011, pp. 47–61.

    Chapter  Google Scholar 

  21. Zheng, Z., Wu, X., Zhang, Y., Lyu, M.R., and Wang, J., QoS ranking prediction for cloud services, IEEE Trans. Parallel Distrib. Syst., 2013, vol. 24, pp. 1213–1222.

    Article  Google Scholar 

  22. Wang, S., Zheng, Z., Wu, Z., Lyu, M.R., and Yang, F., Reputation measurement and malicious feedback rating prevention in web service recommendation systems, IEEE Trans. Serv. Comput., 2015, vol. 8, pp. 755–767.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Manonmaniam Sundaranar University, Tirunelveli, Tamilnadu, 627012, India

    S. Senthil Kumar

  2. School of Computing Science and Engineering (SCSE), VIT University, Vellore, Tamilnadu, 632014, India

    S. Margret Anouncia

Authors
  1. S. Senthil Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Margret Anouncia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Senthil Kumar.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Senthil Kumar, S., Margret Anouncia, S. QoS-Based Concurrent User-Service Grouping for Web Service Recommendation. Aut. Control Comp. Sci. 52, 220–230 (2018). https://doi.org/10.3103/S0146411618030070

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411618030070

Keywords

Search

Navigation