Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-$n$-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.
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