论文提出一种混合架构，用大模型和小模型结合，将电商搜索发现覆盖率从60%提升到80%，推理成本仅大模型的30%，适合大规模部署。
该论文提出一种发现增强的搜索系统，利用意图条件召回扩展。采用两阶段混合架构：先使用闭源大语言模型（LLM）优化头部查询，再通过LoRA适配器和师生蒸馏微调小语言模型（SLM）覆盖尾部查询。系统评估使用LLM-as-a-judge指标和端到端会话级购买分析。结果将发现覆盖率从约60%提升至80%，推理成本仅为教师模型的30%。
Improving Item Discoverability in e-Commerce Search via Related Intent Generation
Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce marketplaces and particularly grocery, this paradigm is limiting, as user satisfaction and commercial outcomes depend heavily on the discoverability of substitute, complementary, and thematically related items. In this paper, we present a scalable system for discovery-augmented search that leverages intent-conditioned recall expansion. Our approach generates implicit user intents to expand candidate recall while maintaining relevance. The system addresses the cost-quality tradeoff of generative retrieval through a two-stage hybrid architecture. First, we leverage closed-weight large language models (LLMs) to maximize discoverability for head queries. To extend these benefits to tail queries, we then introduce a finetuned small language model (SLM), trained via LoRA adapters and teacher-student distillation. We evaluate the system using a rigorous dual framework: (a) LLM-as-a-judge metrics validated against human preferences for semantic quality, and (b) end-to-end session-level purchase analysis. Results demonstrate that our approach improves both intent generation quality and downstream retrieval effectiveness, extending discovery coverage from approximately 60% to 80% of query traffic at roughly 30% of the teacher model's inference cost, offering a viable path for deployment in large-scale marketplaces. Beyond relevance gains, discovery-augmented search may serve as a marketplace-balancing mechanism, giving long-tail and emerging supply an opportunity for query-conditioned exposure.