  Modern recommender systems may output considerably different recommendations
due to small perturbations in the training data. Changes in the data from a
single user will alter the recommendations as well as the recommendations of
other users. In applications like healthcare, housing, and finance, this
sensitivity can have adverse effects on user experience. We propose a method to
stabilize a given recommender system against such perturbations. This is a
challenging task due to (1) the lack of a ``reference'' rank list that can be
used to anchor the outputs; and (2) the computational challenges in ensuring
the stability of rank lists with respect to all possible perturbations of
training data. Our method, FINEST, overcomes these challenges by obtaining
reference rank lists from a given recommendation model and then fine-tuning the
model under simulated perturbation scenarios with rank-preserving
regularization on sampled items. Our experiments on real-world datasets
demonstrate that FINEST can ensure that recommender models output stable
recommendations under a wide range of different perturbations without
compromising next-item prediction accuracy.
