关键词: Web服务, 服务质量（QoS）预测, 张量分解, 贝叶斯算法

Abstract: With the maturity of Web services related standards and technologies, Web services recommendation based on Quality of Service（QoS） plays a decisive role in user experience. How to accurately predict the Qos value is a hot research topic today. In the past, collaborative filtering algorithms based on neighbors or models use two-dimensional information of “user-service”. The predicted QoS value is static and not accurate. The dimension of time information is introduced into the tensor model, and the three-dimensional tensor of “user service time” can make the QoS prediction more consistent with the user’s requirements. The Bayesian method is used to solve the tensor decomposition. The introduction of probabilistic interpretation and analysis of the system provides a Bayesian inference framework for priori probabilities, which improves the accuracy of QoS prediction. Experiments show that the prediction results using this algorithm have smaller mean absolute deviation than other algorithms, and solve the problem of data sparsity.

Key words: Web services, Quality of Service（QoS） prediction, tensor decomposition, Bayesian algorithm