用多智能体系统让电商搜索从找商品变为主动帮用户做购物决策。
Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search
- 构建多智能体框架，模拟用户多阶段决策过程，主动提供购物建议。
- 在复杂查询上推荐准确率显著提升，用户满意度更高，线上测试效果稳定。
- 适合需要专业导购、处理复杂需求的电商场景，如京东等大型平台。
传统电商搜索依赖检索-排序范式，但其仅基于查询与商品匹配，无法契合用户复杂的多阶段认知决策过程。这种错位导致复杂查询存在语义鸿沟、跨平台信息搜寻成本高、缺乏专业购物指导等问题。为此，我们提出多智能体认知决策框架（MACDF），将搜索范式从被动检索转向主动决策支持。离线评估显示，该框架在涉及否定、多约束或推理需求的复杂查询中，显著提升了推荐准确率与用户满意度。在线A/B测试在京东搜索平台验证了其实际有效性。本工作揭示了多智能体认知系统在重构电商搜索中的变革潜力。
原文摘要 · Abstract (English)
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.
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