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

arXiv: https://arxiv.org/abs/2510.20567 · HTML: https://mlsanity.com/paper/2510.20567/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Builds on (most cited first, 12 of 12)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Offline Reinforcement Learning with Implicit Q-Learning](https://mlsanity.com/paper/2110.06169/) (2021)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [Large language models can accurately predict searcher preferences](https://mlsanity.com/paper/2309.10621/) (2023)
- [From Matching to Generation: A Survey on Generative Information Retrieval](https://mlsanity.com/paper/2404.14851/) (2024)
- [Embedding-based Product Retrieval in Taobao Search](https://mlsanity.com/paper/2106.09297/) (2021)
- [Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplace](https://mlsanity.com/paper/2302.11052/) (2023)
- [Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search](https://mlsanity.com/paper/2303.11009/) (2023)
- [Knowledge Distillation based Contextual Relevance Matching for E-commerce Product Search](https://mlsanity.com/paper/2210.01701/) (2022)
- [Embedding based retrieval for long tail search queries in ecommerce](https://mlsanity.com/paper/2505.01946/) (2025)
- [Beyond Keywords and Relevance: A Personalized Ad Retrieval Framework in E-Commerce Sponsored Search](https://mlsanity.com/paper/1712.10110/) (2017)

## 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.