论文标题
TOP-K推荐系统的轻松分类操作的可区分排名指标
A Differentiable Ranking Metric Using Relaxed Sorting Operation for Top-K Recommender Systems
论文作者
论文摘要
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，并过滤高分的TOP-K项目来为用户生成个性化建议。虽然对此建议过程的排序和排名项目是不可或缺的，但是将它们纳入端到端模型培训的过程并不容易，因为排序是非不同的，并且很难通过梯度下降来优化。这造成了现有学习目标与推荐人的排名指标之间的不一致问题。在这项工作中，我们提出了DRM（可区分的排名指标），从而减轻不一致并通过采用可区分的排名指标的放松来提高建议性能。通过使用几个现实世界数据集的实验，我们证明了与其他最先进的建议方法相比，对现有因子推荐人的联合学习显着提高了建议质量。
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.
