The paper introduces stability as a new measure of the recommender systems performance. In general, we define a recommendation algorithm to be "stable" if its predictions for the same items are consistent over a period of time, assuming that any new ratings that have been submitted to the recommender system over the same period of time are in complete agreement with system's prior predictions. In this paper, we advocate that stability should be a desired property of recommendation algorithms, because unstable recommendations can lead to user confusion and, therefore, reduce trust in recommender systems. Furthermore, we empirically evaluate stability of several popular recommendation algorithms. Our results suggest that model-based recommendation techniques demonstrate higher stability than memory-based collaborative filtering heuristics. We also find that the stability measure for recommendation techniques is influenced by many factors, including the sparsity of the initial rating data, the number of new incoming ratings (representing the length of the time period over which the stability is being measured), the distribution of the newly added rating values, and the rating normalization procedures employed by the recommendation algorithms.