Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search

TL;DR

This paper introduces MACDF, a multi-agent cognitive framework that enhances e-commerce search by supporting complex decision-making, leading to improved recommendation accuracy and user satisfaction over traditional retrieval-ranking methods.

Contribution

It presents a novel multi-agent cognitive decision framework that shifts from passive retrieval to proactive decision support in e-commerce search.

Findings

Significant improvements in recommendation accuracy.

Enhanced user satisfaction for complex queries.

Validated effectiveness through online A/B testing.

Abstract

The retrieval-ranking paradigm has long dominated e-commerce search, but its reliance on query-item matching fundamentally misaligns with multi-stage cognitive decision processes of platform users. This misalignment introduces critical limitations: semantic gaps in complex queries, high decision costs due to cross-platform information foraging, and the absence of professional shopping guidance. To address these issues, we propose a Multi-Agent Cognitive Decision Framework (MACDF), which shifts the paradigm from passive retrieval to proactive decision support. Extensive offline evaluations demonstrate MACDF's significant improvements in recommendation accuracy and user satisfaction, particularly for complex queries involving negation, multi-constraint, or reasoning demands. Online A/B testing on JD search platform confirms its practical efficacy. This work highlights the transformative potential of multi-agent cognitive systems in redefining e-commerce search.