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Journal of Electrical and Computer Engineering
Volume 2017 (2017), Article ID 9547869, 10 pages
Research Article

Dynamically Predicting the Quality of Service: Batch, Online, and Hybrid Algorithms

University of Science and Technology, Beijing 100080, China

Correspondence should be addressed to Zhong-an Jiang

Received 10 August 2016; Accepted 14 February 2017; Published 6 March 2017

Academic Editor: Jar Ferr Yang

Copyright © 2017 Ya Chen and Zhong-an Jiang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper studies the problem of dynamically modeling the quality of web service. The philosophy of designing practical web service recommender systems is delivered in this paper. A general system architecture for such systems continuously collects the user-service invocation records and includes both an online training module and an offline training module for quality prediction. In addition, we introduce matrix factorization-based online and offline training algorithms based on the gradient descent algorithms and demonstrate the fitness of this online/offline algorithm framework to the proposed architecture. The superiority of the proposed model is confirmed by empirical studies on a real-life quality of web service data set and comparisons with existing web service recommendation algorithms.