Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamczak, R., et al.: Accurate prediction of solvent accessibility using neural networks-based regression. Proteins Struct. Funct. Bioinform. 56(4), 753–767 (2004)
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)
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)
Chattopadhyay, S., et al.: A framework for top service subscription recommendations for service assemblers. In: IEEE SCC, pp. 332–339 (2016)
Chen, X., et al.: Personalized qos-aware web service recommendation and visualization. IEEE TSC 6(1), 35–47 (2013)
Daniel, G.: Principles of Artificial Neural Networks, vol. 7. World Scientific, Singapore (2013)
Demuth, H., Beale, M.: Neural Network Toolbox, vol. 4. The MathWorks Inc., Boston (2004)
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)
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)
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)
Lo, W., et al.: An extended matrix factorization approach for qos prediction in service selection. In: IEEE SCC, pp. 162–169. IEEE (2012)
Ma, Y., et al.: Predicting QoS values via multi-dimensional QoS data for web service recommendations. In: ICWS, pp. 249–256. IEEE (2015)
Qi, K., et al.: Personalized QoS prediction via matrix factorization integrated with neighborhood information. In: SCC, pp. 186–193. IEEE (2015)
Sarwar, B.M., et al.: Item-based collaborative filtering recommendation algorithms. WWW 1, 285–295 (2001)
Shao, L., et al.: Personalized qos prediction for web services via collaborative filtering. In: IEEE ICWS, pp. 439–446 (2007)
Sun, H., et al.: Personalized web service recommendation via normal recovery collaborative filtering. IEEE TSC 6(4), 573–579 (2013)
Tang, M., et al.: Location-aware collaborative filtering for QoS-based service recommendation. In: ICWS, pp. 202–209. IEEE (2012)
Wang, S., et al.: Multi-dimensional QoS prediction for service recommendations. IEEE TSC 12, 47–57 (2016)
Wu, C., Qiu, W., et al.: Time-aware and sparsity-tolerant QoS prediction based on collaborative filtering. In: IEEE ICWS, pp. 637–640 (2016)
Wu, H., et al.: Collaborative QoS prediction with context-sensitive matrix factorization. Future Gener. Comp. Syst. 82, 669–678 (2018)
Wu, X., et al.: Collaborative filtering service recommendation based on a novel similarity computation method. IEEE TSC 10(3), 352–365 (2017)
Zheng, Z., et al.: QoS-aware web service recommendation by collaborative filtering. IEEE TSC 4(2), 140–152 (2011)
Zheng, Z., et al.: Collaborative web service QoS prediction via neighborhood integrated matrix factorization. IEEE TSC 6(3), 289–299 (2013)
Zheng, Z., et al.: Investigating qos of real-world web services. IEEE TSC 7(1), 32–39 (2014)
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). https://doi.org/10.1007/978-3-030-03596-9_31
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Chattopadhyay, S., Banerjee, A. (2019). QoS Value Prediction Using a Combination of Filtering Method and Neural Network Regression. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-33702-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33701-8
Online ISBN: 978-3-030-33702-5
eBook Packages: Computer ScienceComputer Science (R0)