Collaborative Web Service QoS Prediction on Unbalanced Data Distribution

QoS prediction is critical to Web service selection and recommendation. This paper proposes a collaborative approach to quality-of-service (QoS) prediction of web services on unbalanced data distribution by utilizing the past usage history of service users. It avoids expensive and time-consuming web service invocations. There existed several methods which search top-k similar users or services in predicting QoS values of Web services, but they did not consider unbalanced data distribution. Then, we improve existed methods in similar neighbors´ selection by sampling importance resampling. To validate our approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than other approaches.