Personalized QoS Prediction via Matrix Factorization Integrated with Neighborhood Information

Currently, the number of Web services on the Internet is growing exponentially. Faced with a large number of functionally equivalent candidate services, users always hope to select the optimal one that can provide the best QoS values. However, users usually do not know the QoS values of all the candidate services as the limited historical service invocation records. Different QoS prediction methods are presented to predict QoS values of candidate services. Nevertheless, most of them do not take the physical features of Web services fully into consideration and thus the prediction accuracy is still not satisfying. To this end, we propose a novel Matrix Factorization method, integrating both user network neighborhood information and service neighborhood information with Matrix Factorization model, to predict personalized QoS values. To validate our method, experiments are conducted on a real-world Web service QoS dataset including 1,974,675 Web service invocation records. Experimental results show that our method performs better in prediction accuracy than the state-of-the-art methods.