Malicious and spam actions in online rating systems affect the user reputation measurements greatly.By setting different number of spammers and evaluating its effect by the root-mean-square error (RMSE),the robustness of three typical iterative-oriented online user reputation measurements was investigated.The results for MovieLens and Netflix data sets show that when facing with 1%~60% of spammers in the network,the CR algorithm has the best performance of robustness.The largest RMSE value of the iterative algorithm of reputation IARR reaches 0.22,with slight fluctuation of the RMSE.And the RMSE value of the improved iterative algorithm of reputation IARR2 reaches 0.695.The result for Douban data set shows that the CR algorithm still maintains great robustness even when the users rate few common items.