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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
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)
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)
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)
Zeng, L., Benatallah, B., Dumas, M., Kalagnanam, J., Sheng, Q.Z.: Quality driven web services composition. In: WWW, pp. 411–421, (2003)
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)
Menasce, D.: Qos issues in web services. J. IEEE Internet Comput. 6, 72–75 (2002)
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)
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)
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)
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)
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)
Burke, R.: Hybrid recommender systems: survey and experiments. J. User Model. User-Adap. Interact. 12, 331–370 (2002)
Hofmann, T.: Latent semantic models for collaborative filtering. J. Acm Trans. Inf. Syst. 22, 89–115 (2004)
Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. In: SIAM Data Mining (SDM 2005), pp. 471–480 (2005)
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)
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)
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)
Breese, J. S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
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)
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)
Gao, M., Wu, Z., Jiang, F.: Userrank for item-based collaborative filtering recommendation. J. Inf. Process. Lett. 111, 444–446 (2011)
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)
Zheng, Z., Michael R.L.: Component recommendation for cloud applications. In: 2nd International Workshop on Recommendation Systems for Software Engineering, pp. 323–345 (2010)
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)
Gao, M., Fu, Y., Chen, Y., Jiang, F.: User-weight model for item-based recommendation systems. J. Softw. 7, 2133–2140 (2012)
Liu, R., Jia, C., Zhou, T.: Personal recommendation via modified collaborative filtering. J. Phys. A Stat. Mech. Appl. 388, 462–468 (2009)
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)
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)