FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
This addresses stability issues in recommender systems for applications like healthcare, housing, and finance, though it is incremental as it builds on existing fine-tuning techniques.
The paper tackles the problem of recommender systems being sensitive to small data perturbations, which can lead to unstable recommendations, and proposes FINEST, a method that stabilizes recommendations through rank-preserving fine-tuning while maintaining next-item prediction accuracy on real-world datasets.
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.