Multi-QoS Effective Prediction in Web Service Selection

The rising development of service-oriented architecture makes Web service selection a hot research topic. However, there still remains challenges to design accurate personalized QoS prediction approaches for Web service selection, as existing algorithms are all focused on predicting individual QoS, without considering the relationship between them. In this paper, we propose a novel Multi-QoS Effective Prediction (MQEP for short) problem, which aims to make effective Multi-QoS prediction based on Multi-QoS attributes and their relationships. To address this problem, we design a novel prediction framework Multi-QoS Effective Prediction Approach (MQEPA for short). MQEPA first takes use of Gaussian method to normalize the QoS attribute values, then exploits Non-negative Matrix Factorization to extract the feature of Web services from Multi-QoS attributes, and last predicts the Multi-QoS of unused services via Multi-output Support Vector Regression algorithm. Comprehensive empirical studies demonstrate the utility of the proposed method.