Requirement--Evidence Alignment for Compositional E-Commerce Queries

2026-08-03

AI Summary

This work addresses the limitation of existing e-commerce reranking models that oversimplify multi-constraint queries into a single overall relevance score, often yielding recommendations that only partially fulfill users’ explicit requirements and lack evidential support. To overcome this, we propose REAlign, a novel framework that introduces an explicit demand-evidence alignment mechanism. REAlign models demand types and grounds item-level visible evidence to distinguish between satisfied, violated, and unsupported conditions, thereby constructing demand-oriented contrastive samples and a demand-aware groupwise relative policy optimization method. A multidimensional list utility function—incorporating demand satisfaction, evidence support, violation penalties, and output validity—is designed to transcend conventional aggregated relevance paradigms. Experiments on two e-commerce benchmarks demonstrate that REAlign significantly outperforms strong supervised and policy optimization baselines, particularly improving top-rank quality and compliance of top results, with ablation studies confirming the complementary effectiveness of each component.

Abstract

Compositional e-commerce queries express multiple requirements that must hold jointly, yet existing rerankers collapse these constraints into aggregate relevance and often promote topical near misses over feasible products. In this paper, we introduce REAlign, a novel requirement-evidence-aligned reranking framework that explicitly connects typed query requirements with visible evidence. REAlign distinguishes satisfied, violated, and unsupported conditions, constructs requirement-targeted contrasts that expose failure modes, and optimizes duplicate-free partial rankings through Requirement-Aware Group-Relative Policy Optimization. Its list utility preserves relevance while incorporating requirement satisfaction, evidence support, material violations, and output validity. Experiments on two fixed-pool e-commerce benchmarks show consistent improvements over strong supervised and policy-optimization baselines under matched training budgets, with fewer violations among top-ranked candidates and larger gains at shallow ranks. Controlled ablations confirm the complementary value of requirement modeling, evidence grounding, and decomposed optimization.

Problem

• compositional queries
• requirement-evidence alignment
• e-commerce reranking
• constraint satisfaction
• relevance ranking

Innovation

• requirement-evidence alignment
• compositional e-commerce queries
• reranking framework
• policy optimization
• evidence grounding