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 largescale scenarios has become a popular topic in both industry and academia. The quality of service (QoS) predictionbased 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 detectionbased 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 timeconsuming 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.

Integrating Reinforcement Learning and Skyline Computing for Adaptive Service Composition Abstract   In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoSaware service composition aims to find the optimal composition scheme with the QoS attributes that best match user requirements. However, certain QoS attributes may continuously change in a dynamic service environment, so service composition methods need to be adaptive. Furthermore, the large number of candidate services poses a key challenge for service composition, where existing service composition approaches based on reinforcement learning (RL) suffer from low efficiency. To deal with the problems above, in this paper, a new service composition approach is proposed which combines RL with skyline computing where the latter is used for reducing the search space and computational complexity. A WSCMDP model is proposed to solve the largescale service composition within a dynamically changing environment. To verify the proposed method, a series of comparative experiments are conducted, and the experimental results demonstrate the effectiveness, scalability and adaptability of the proposed approach.