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

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. © 2017 ya chen and zhong-an jiang.