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 choosing services, service reputation should be as close as possible to the true service ranking. In this paper, an online service reputation measurement method for error minimization was proposed and it regarded user preference ranking as a noisy estimation of real service ranking. Firstly, Kendall tau distance was used to measure the error between service ranking and truth ranking. Then, the possible ranking of truth services was 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 minimum average error between itself and the possible sets of service ranking was found as the service reputation. Because all the service ranking could be the truth ranking, causing the computational difficulty of this method, the branch-and-cut algorithm was used to optimize the solution. Based on the real and simulated data sets, experiments were carried out and the result showed that reputation measurement results could be obtained with less error between it and the truth while ensuring the operation efficiency.