CogSearch: A Cognitive-Aligned Multi-Agent Framework for Proactive Decision Support in E-Commerce Search

TL;DR

CogSearch is a multi-agent framework that enhances e-commerce search by proactively supporting complex decision-making, significantly reducing user decision costs and increasing conversion rates through cognitive-aligned processes.

Contribution

It introduces a novel multi-agent system that models human cognition to transform e-commerce search into a proactive decision support tool, surpassing traditional relevance-focused methods.

Findings

Reduced decision costs by 5%

Increased overall UCVR by 0.41%

Boosted conversion rate for decision-heavy queries by 30%

Abstract

Modern e-commerce search engines, largely rooted in passive retrieval-and-ranking models, frequently fail to support complex decision-making, leaving users overwhelmed by cognitive friction. In this paper, we introduce CogSearch, a novel cognitive-oriented multi-agent framework that reimagines e-commerce search as a proactive decision support system. By synergizing four specialized agents, CogSearch mimics human cognitive workflows: it decomposes intricate user intents, fuses heterogeneous knowledge across internal and external sources, and delivers highly actionable insights. Our offline benchmarks validate CogSearch's excellence in consultative and complex search scenarios. Extensive online A/B testing on JD.com demonstrates the system's transformative impact: it reduced decision costs by 5% and achieved a 0.41% increase in overall UCVR, with a remarkable 30% surge in conversion for decision-heavy queries. CogSearch represents a fundamental shift in information retrieval, moving beyond traditional relevance-centric paradigms toward a future of holistic, collaborative decision intelligence.