Large-scale e-commerce search must surface a broad set of items from a vast catalog, ranging from bestselling products to new, trending, or seasonal items. Modern systems therefore rely on multiple specialized retrieval channels to surface products, each designed to satisfy a specific objective. A key challenge is how to effectively merge documents from these heterogeneous channels into a single ranked list under strict latency constraints while optimizing for business KPIs such as user conversion. Rank-based fusion methods such as Reciprocal Rank Fusion (RRF) and Weighted Interleaving rely on fixed global channel weights and treat channels independently, failing to account for query-specific channel utility and cross-channel interactions. We observe that multi-channel fusion can be reformulated as a query-dependent learning-to-rank problem over heterogeneous candidate sources. In this paper, we propose a unified ranking model that learns to merge and rank documents from multiple retrieval channels. We formulate the problem as a channel-aware learning-to-rank task that jointly optimizes clicks, add-to-carts, and purchases while incorporating channel-specific objectives. We further incorporate recent user behavioral signals to capture short-term intent shifts that are critical for improving conversion in multi-channel ranking. Our online A/B experiments show that the proposed approach outperforms rank-based fusion methods, leading to a +2.85\% improvement in user conversion. The model satisfies production latency requirements, achieving a p95 latency of under 50\,ms, and is deployed on Target.com.

翻译：大规模电商搜索必须从海量商品库中筛选出广泛的产品集合，涵盖畅销商品、新品、热门商品及季节性商品。因此，现代系统依赖多个专用检索通道来呈现商品，每个通道均针对特定目标而设计。一个关键挑战在于如何在严格的延迟约束下，将来自这些异构通道的文档有效合并为单一排序列表，同时优化用户转化等业务关键绩效指标。基于排序的融合方法（如 Reciprocal Rank Fusion 和 Weighted Interleaving）依赖固定的全局通道权重，且将各通道独立处理，未能考虑查询特定的通道效用及跨通道交互。我们观察到，多通道融合可被重新表述为基于异构候选源的查询依赖性排序学习问题。本文提出一种统一排序模型，该模型学习对来自多个检索通道的文档进行合并与排序。我们将该问题建模为通道感知的排序学习任务，在联合优化点击、加购及购买行为的同时，融入通道特定的目标。我们进一步引入近期用户行为信号，以捕捉短期意图变化，这对提升多通道排序中的转化率至关重要。在线 A/B 实验表明，所提方法优于基于排序的融合方法，用户转化率提升 +2.85%。该模型满足生产环境延迟要求，p95 延迟低于 50 ms，并已在 Target.com 上线部署。