Services recommended trust algorithm based on cloud model attributes weighted clustering

Abstract

There are many different cloud services available, each with different offerings and standards of quality. Choosing a credible and reliable service has become a key issue. To address the shortcomings of existing evaluation methods, we propose a service clustering method based on weighted cloud model attributes. We calculate user-rating similarity with the weighted Pearson correlation coefficient method based on service clustering, and then compute user similarity combined with the user service selection index weight. This method allows us to determine the nearest neighbors. Finally, we obtain the recommended trust of the service for the target user through the recommendation trust algorithm. Simulation results show that the proposed algorithm can more accurately calculate service recommended trust. This method meets the demand of users in terms of service trust, and it improves the success rate of user service selection.