FINEST：通过保秩微调稳定推荐结果
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
AI总结：
FINEST通过从推荐模型获取参考排名列表，并在模拟扰动下用保秩正则化微调模型，以稳定推荐输出，同时保持预测准确性。
摘要：
现代推荐系统可能因训练数据中的微小扰动而输出差异相当大的推荐结果。来自单个用户的数据变化将改变该用户的推荐结果，同时也会改变其他用户的推荐结果。在医疗保健、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。我们提出一种方法，用于稳定给定推荐系统免受此类扰动的影响。这是一项具有挑战性的任务，原因在于：（1）缺乏可用于锚定输出的“参考”排名列表；（2）在确保排名列表相对于训练数据所有可能扰动的稳定性方面存在计算挑战。我们的方法FINEST通过从给定推荐模型获取参考排名列表，然后在模拟扰动场景下对采样项目施加保秩正则化进行模型微调，从而克服了这些挑战。我们在真实数据集上的实验表明，FINEST能够确保推荐模型在多种不同扰动下输出稳定的推荐结果，同时不损害下一项预测的准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
