Utilizing customer satisfaction in ranking prediction for personalized cloud service selection

摘要

With the rapid development of cloud computing, cloud service has become an indispensable component of modern information systems where quality of service (QoS) has a direct impact on the system's performance and stability. While scholars have concentrated their efforts on the monitoring and evaluation of QoS in cloud computing, other service selection characteristics have been neglected, such as the scarcity of evaluation data and various customer needs. In this paper, we present a ranking-oriented prediction method that will assist in the process of discovering the cloud service candidates that have the highest customer satisfaction. This approach encompasses two basic functions: ranking similarity estimation and cloud service ranldng prediction that takes into account customer's preference and expectation. The comparative experimental results show that the proposed method outperforms other competing methods. (C) 2016 Elsevier B.V. All rights reserved.