🤖 AI Summary
This work addresses the challenge of effectively fusing multiple heterogeneous retrieval channels under strict latency constraints to optimize business metrics such as user conversion. We propose a channel-aware unified learning-to-rank framework that formulates multi-channel result fusion as a query-dependent multi-objective ranking problem, jointly optimizing for click-through, add-to-cart, and purchase outcomes. The approach explicitly incorporates channel-specific signals and users’ short-term behavioral sequences, and leverages query-adaptive fusion strategies alongside cross-channel interaction modeling to overcome the limitations of conventional fixed-weight fusion methods. Online A/B experiments demonstrate that the system achieves a 2.85% improvement in user conversion rate while maintaining a p95 latency below 50 milliseconds, and has been successfully deployed in the production environment of Target.com.
📝 Abstract
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.