The goal of this master project is to develop a model for stabilizing the ranking of the results in time, so that their variations are acceptable to a user, making a trade-off between conservatism and novelty, and maximizing relevance at every step. The work will use recent approaches to ranking in information retrieval known as learning-to-rank, and will include also the development of metrics for assessing the quality and usefulness of the proposed stabilized ranking model.