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

arXiv: https://arxiv.org/abs/2602.23530 · HTML: https://mlsanity.com/paper/2602.23530/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation](https://mlsanity.com/paper/2608.05819/) (2026)

## Abstract (arXiv)

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.