LAUSR

In recent years, as the number of Web services, increases dramatically, the personalized Web service recommendation has become a hot topic in both academia and industry. The quality-of-service (QoS) prediction plays a key role in Web service recommendation systems. However, how to further improve the accuracy of QoS prediction is still a problem. Traditional QoS predicting models do not consider the impact of sampling methods on the accuracy of QoS prediction. However, the outstanding sampling method can train the predicting model more effectively and obtain higher accuracy. Therefore, it is necessary to study sampling methods based on the QoS dataset in order to obtain sample distribution closer to the original distribution, so as to improve the accuracy of the predicting models. In this paper, we first discuss how to apply several existing sampling methods to QoS datasets and then analyze their advantages and disadvantages. Finally, a novel sampling method, enhanced importance resampling (EIRS), is proposed and applied. The experiments on the real-world datasets show that our method can not only sample efficiently and accurately but also can greatly improve the accuracy of Web service QoS prediction.