Beyond Retrieval-Ranking: A Multi-Agent Cognitive Decision Framework for E-Commerce Search
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 40 references that are in the map. First posted on arXiv 23 Oct 2025.
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References · 12
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
- 2021Offline Reinforcement Learning with Implicit Q-Learningcited by 1,245
- 2020RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answeringcited by 410
- 2023Large language models can accurately predict searcher preferencescited by 129
- 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
- 2021Embedding-based Product Retrieval in Taobao Searchcited by 50
- 2023Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplacecited by 9
- 2023Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Searchcited by 9
- 2022Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Searchcited by 7
- 2025Embedding based retrieval for long tail search queries in ecommercecited by 3
- 2017Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Searchcited by 2
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