An Adaptive Rating System for Service Computing
Xinfeng Ye, Jupeng Zheng · 2013
Many service rating systems have been proposed for service computing to help users select services. Most of these systems do not consider the unfair rating problem. As a result, malicious users and services might explore the weakness of the existing rating systems in handling unfair ratings to gain commercial advantage. This paper proposed a service rating scheme that is robust against manipulations by malicious users and services. The system helps a customer to choose a suitable service through predicting the customer's ratings to services. When predicting a customer's rating for a service, the system uses the ratings given to the service by the experienced users and the users that are similar to the customer. Simulation results showed that (a) compared with other schemes, the proposed system has good prediction accuracy, and (b) the system tackles the unfair rating problem effectively.