Fusion of Pearson similarity and Slope One methods for QoS prediction for web services

Web services have become the primary source for constructing software system over Internet. The quality of whole system greatly dependents on the QoS of single Web service, so QoS information is an important indicator for service selection. In reality, QoSs of some Web services may be unavailable for users. How to predicate the missing QoS value of Web service through fully using the existing information is a difficult problem. This paper attempts to settle this difficulty by fusing Pearson similarity and Slope One methods for QoS prediction. In this paper, the Pearson similarity is adopted between two services as the weight of their deviation. Meanwhile, some strategies like weight adjustment and SPC-based smoothing are also utilized for reducing prediction error. In order to evaluate the validity of the proposed algorithm, comparative experiments are performed on the real-world data set. The result shows that the proposed algorithm exhibits better prediction precision than both basic Slope One and the well-known WsRec algorithm in most cases. Meanwhile, the new approach has the strong ability of reducing the impact of noise data.