Top-K推荐系统中基于松弛排序操作的可微排序度量
A Differentiable Ranking Metric Using Relaxed Sorting Operation for Top-K Recommender Systems
摘要：
推荐系统通过计算项目的偏好分数、根据分数对项目进行排序以及过滤具有高分数的前K个项目来为用户生成个性化推荐。虽然对项目进行排序和排名是这个推荐过程不可或缺的一部分，但将它们纳入端到端模型训练的过程并非易事，因为排序是不可微的，并且难以用梯度下降进行优化。这导致了现有的学习目标和排名指标之间的不一致性问题。在这项工作中，我们提出了DRM（微分排名度量），减轻了不一致性，并通过采用微分放松排名度量，提高推荐性能。通过与几个真实世界的数据集的实验，我们证明了DRM目标的联合学习现有的基于因子的推荐器显着提高了推荐的质量，与其他国家的最先进的推荐方法相比。
英文摘要：
A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K items with high scores. While sorting and ranking items are integral for this recommendation procedure, it is nontrivial to incorporate them in the process of end-to-end model training since sorting is nondifferentiable and hard to optimize with gradient descent. This incurs the inconsistency issue between existing learning objectives and ranking metrics of recommenders. In this work, we present DRM (differentiable ranking metric) that mitigates the inconsistency and improves recommendation performance by employing the differentiable relaxation of ranking metrics. Via experiments with several real-world datasets, we demonstrate that the joint learning of the DRM objective upon existing factor based recommenders significantly improves the quality of recommendations, in comparison with other state-of-the-art recommendation methods.
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。
