Utilizing customer satisfaction in ranking prediction for personalized cloud service selection

•We present a ranking-oriented prediction method intended to assist in discovering cloud service candidates with highest customer satisfaction.•Our proposed approach encompasses both ranking similarity estimation and cloud service ranking prediction, considering customer preferences and expectations.•A customer satisfaction function is presented to determine preference values regarding pairs of services.•We show that the proposed method outperforms other competing methods.

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 ranking prediction that takes into account customer's preference and expectation. The comparative experimental results show that the proposed method outperforms other competing methods.