Deep recommender systems (DRS) are intensively applied in modern web services. To deal with the massive web contents, DRS employs a two-stage workflow: retrieval and ranking, to generate its recommendation results. The retriever aims to select a small set of relevant candidates from the entire items with high efficiency; while the ranker, usually more precise but time-consuming, is supposed to further refine the best items from the retrieved candidates. Traditionally, the two components are trained either independently or within a simple cascading pipeline, which is prone to poor collaboration effect. Though some latest works suggested to train retriever and ranker jointly, there still exist many severe limitations: item distribution shift between training and inference, false negative, and misalignment of ranking order. As such, it remains to explore effective collaborations between retriever and ranker.

翻译：深度推荐系统（DRS）广泛应用于现代网络服务中。为处理海量网络内容，DRS采用两阶段工作流程：检索与排序，以生成推荐结果。检索器旨在从全量物品中高效筛选出少量相关候选集；而排序器（通常更精确但计算耗时）则负责从检索候选集中进一步精炼出最佳物品。传统上，这两个组件要么独立训练，要么嵌入简单级联流水线，这容易导致协同效果不佳。尽管近期部分研究提出对检索器和排序器进行联合训练，但仍存在诸多严重限制：训练与推理间的物品分布偏移、假负例问题以及排序顺序错位。因此，如何实现检索器与排序器的有效协同仍有待探索。