Collaborative Web Service Quality Prediction via Network Biased Matrix Factorization

Abstract

Facing a large number of candidate Web service with the same function, user wishes to get the most appropriate one. Quality-of-Service (QoS) which represents non-functional attributes of Web services, has become a major concern for choosing service. But it is time-consuming and resource-consuming to assess all the QoS values by invoking candidate services one by one. Thus, QoS prediction is considered an effective method to obtain QoS information. Although most of QoS prediction methods claim be able to capture the interaction between users and services, few of them take account non-interaction factors, especially the factors arising from the network environment. In this paper, the non-interaction factors from the network environment are referred as network bias, and a network biased matrix factorization (NBMF) method is proposed for QoS prediction. The method packages network bias into a linear regression model and puts the user-service interaction into a matrix factorization model, which is more sophisticated in adapting diversified circumstance, particularly in complex network environment. In addition, extensive experiments are conduct on real-world QoS dataset, and the result prove that the NBMF method achieves better performance than other state-ofthe-art methods.