Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.

翻译：排序学习是top-N推荐任务中的核心技术，理想的排序器应是从物品集合到排列（即全排列）的映射。现有大多数解决方案遵循概率排序原则（PRP）范式，即先对候选集中的每个物品进行评分，再进行排序操作生成top-N列表。然而，这些方法在个体评分过程中忽略了候选物品间的上下文依赖关系，且排序操作不可微。为规避上述问题，我们提出集合到排列排序（STARank）这一新框架，该框架无需个体评分和排序操作即可直接生成候选物品的排列，并且是端到端可微的。因此，STARank可在仅访问真实排列而无需物品真实相关性分数的情况下运行。为此，STARank首先在用户浏览历史背景下读取候选物品，其表征被输入Plackett-Luce模块以将指定物品排列为列表。为有效利用给定真实排列监督STARank，我们利用Plackett-Luce模型的内部一致性属性推导出计算高效的列表级损失函数。在2个排序学习基准数据集和3个真实世界top-N推荐数据集上与9种最先进方法的实验对比表明，STARank在传统排序指标上具有优越性。值得注意的是，这些排序指标未考虑列表中物品间上下文依赖关系的影响，因此我们设计了新的基于模拟的排序指标族，其中现有指标可视为其特例。在PBM和UBM模拟指标上，STARank均能持续取得更优表现。