Since each online service can be objectively compared by its own real quality, there is a potential truth ranking of services. In order to provide users with the most authentic and objective online service reputation ranking, as a reference for users when choosing services. service reputation should be as close as possible to the true service ranking. Therefore, an online service reputation measurement method for error minimization is proposed. This method regards user preference ranking as a noisy estimation of real service ranking. Firstly, Kendall tau distance is used to measure the error between service ranking and truth ranking. Then, the possible ranking of truth services is found by setting the upper limit of the average error between the truth and the user's preference ranking set. Finally, the service ranking with the lowest average error between the possible set of service ranking and the possible set of service ranking is found as the service reputation. Because all the service ranking may be the truth ranking, the computational difficulty of this method is caused. The Branch-and-Cut algorithm is used to optimize the solution. Based on the real and simulated data sets, experiments show that this method can obtain the credibility measurement results with less error with the truth while ensuring the operation efficiency.