An Extended Matrix Factorization Approach for QoS Prediction in Service Selection

With the growing adoption of Web services on the World Wide Web, the issue of QoS-based service selection is becoming important. A common hypothesis of previous research is that the QoS information to the current user is supposed all known and accurate. However, the real case is that there are many missing QoS values in history records. To avoid the expensive and costly Web services invocations, this paper proposes an extended Matrix Factorization (EMF) framework with relational regularization to make missing QoS values prediction. We first elaborate the Matrix Factorization (MF) model from a general perspective. To collect the wisdom of crowds precisely, we employ different similarity measurements on user side and service side to identify neighborhood. And then we systematically design two novel relational regularization terms inside a neighborhood. Finally we combine both terms into a unified MF framework to predict the missing QoS values. To validate our methods, experiments on real Web services data are conducted. The empirical analysis shows that our approaches outperform other state-of-the-art methods in QoS prediction accuracy.