Web Service Selection Based on QoS Prediction for Clustering and Ranking Services Using Auto-Encoder and K-Means

When selecting web services, users look for those that meet their requirements, primarily the overall functionality and non-functionality quality of service (QoS). In general, various service providers offer a large number of functionally similar services. That makes it very hard for users to find the best ones that satisfy their needs. Thus, service selection based on QoS has emerged as a challenging problem in service computing. So, the authors propose in this paper a web service selection method based on QoS prediction for clustering and ranking services using auto-encoder and k-means. Experiment results show that the proposed method efficiently improves the services' selection accuracy.