A probability distribution detection based hybrid ensemble QoS prediction approach

Abstract Recently, with the rapid increase in the number of web services, how to discover services to meet personalized requirements of users in large-scale scenarios has become a popular topic in both industry and academia. The quality of service (QoS) prediction-based web service recommendation system has been widely studied as an effective method for solving this problem. However, due to the sparsity and imbalance of the observed QoS data, existing QoS prediction approaches suffer from limited prediction accuracy and poor scalability. Moreover, existing QoS prediction approaches do not consider the probability distribution of the observed QoS data, which have remarkable impacts on the prediction performance. In this paper, we propose a novel probability distribution detection-based hybrid ensemble QoS prediction model that can dynamically integrate a set of basic prediction models to improve the prediction accuracy instead of designing complex and time-consuming models. Specifically, we first propose an enhanced CF (collaborative filtering)-based approach as the basis of the prediction model. Second, given the results of a set of other basic prediction models, in addition, we propose a distribution detection algorithm to calculate the PCWs (probability confidence weights) of those results. Finally, we combine them dynamically to obtain final results based on PCWs. Experiments based on real datasets show that our approach has higher prediction accuracy and better scalability than existing mainstream QoS prediction approaches.