We see widespread adoption of slate recommender systems, where an ordered item list is fed to the user based on the user interests and items' content. For each recommendation, the user can select one or several items from the list for further interaction. In this setting, the significant impact on user behaviors from the mutual influence among the items is well understood. The existing methods add another step of slate re-ranking after the ranking stage of recommender systems, which considers the mutual influence among recommended items to re-rank and generate the recommendation results so as to maximize the expected overall utility. However, to model the complex interaction of multiple recommended items, the re-ranking stage usually can just handle dozens of candidates because of the constraint of limited hardware resource and system latency. Therefore, the ranking stage is still essential for most applications to provide high-quality candidate set for the re-ranking stage. In this paper, we propose a solution named Slate-Aware ranking (SAR) for the ranking stage. By implicitly considering the relations among the slate items, it significantly enhances the quality of the re-ranking stage's candidate set and boosts the relevance and diversity of the overall recommender systems. Both experiments with the public datasets and internal online A/B testing are conducted to verify its effectiveness.

翻译：我们观察到版面推荐系统已被广泛采用，即根据用户兴趣和物品内容，向用户提供有序的物品列表。对于每次推荐，用户可从该列表中选择一个或多个物品进行进一步交互。在此场景下，物品间的相互影响对用户行为的显著作用已得到充分认识。现有方法在推荐系统的排序阶段之后增加了版面重排序步骤，通过考虑推荐物品间的相互影响进行重排序并生成推荐结果，以最大化预期整体效用。然而，由于硬件资源限制和系统延迟约束，重排序阶段通常仅能处理数十个候选物品来建模多个推荐物品间的复杂交互。因此，排序阶段对于多数应用而言仍不可或缺，旨在为重排序阶段提供高质量的候选集合。本文提出一种名为"版面感知排序"（SAR）的解决方案，应用于排序阶段。该方案通过隐式考虑版面物品间的关联，显著提升了重排序阶段候选集合的质量，并增强了整体推荐系统的相关性与多样性。我们通过公开数据集实验及内部在线A/B测试验证了其有效性。