Unified Learning-to-Rank for Multi-Channel Retrieval in Large-Scale E-Commerce Search

• URL: http://arxiv.org/abs/2602.23530v2
• Date: Wed, 04 Mar 2026 18:52:54 GMT
• Title: Unified Learning-to-Rank for Multi-Channel Retrieval in Large-Scale E-Commerce Search
• Authors: Aditya Gaydhani, Guangyue Xu, Dhanush Kamath, Ankit Singh, Alex Li,
• Abstract summary: 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.<n>A key challenge is how to effectively merge documents from heterogeneous channels into a single ranked list under strict latency constraints.<n>We propose a unified ranking model that learns to merge and rank documents from multiple retrieval channels.
• Score: 3.4702155400466013
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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