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.

翻译：现代推荐系统可能由于训练数据中的微小扰动而输出显著不同的推荐结果。单个用户数据的变化不仅会改变其自身推荐内容，还会影响其他用户的推荐结果。在医疗、住房和金融等应用中，这种敏感性可能对用户体验产生不利影响。我们提出了一种方法，用于稳定给定推荐系统以抵御此类扰动。该任务面临两大挑战：（1）缺乏可用于锚定输出的“参考”排序列表；（2）确保排序列表对所有可能训练数据扰动具有稳定性的计算难度。我们的方法FINEST通过以下方式克服这些挑战：从给定推荐模型获取参考排序列表，然后在模拟扰动场景下，对采样项目施加保序正则化进行模型微调。在真实数据集上的实验表明，FINEST能够确保推荐模型在广泛扰动条件下输出稳定推荐，且不影响下一项预测精度。