Cloud service recommendation based on trust measurement using ternary interval numbers

Owing to the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers, how to discover the trustworthy cloud services is a challenge for potential users. This paper proposed a cloud service recommendation approach based on trust measurement using ternary interval numbers for potential user. The concept of ternary interval number is introduced. The user feature maybe affecting the QoE evaluations are analyzed and the client-side feature similarity between consumers and potential user is calculated. The transform mechanism from trust evaluations to ternary interval number is presented by employing the K-means clustering algorithm. On the basis of multi-attributes trust aggregation based On FAHP (fuzzy analytic hierarchy process) method, a new possibility degree formula is designed for ranking ternary interval numbers and selecting trustworthy service. Finally, the experiments and results show that this approach is effective to improve the accuracy of the trustworthy service recommendation.