发表机构
Instacart（Instacart）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出两阶段混合架构的发现增强型搜索系统，通过LLM和微调SLM提升电商搜索的商品可发现性，将查询流量发现覆盖率从60%提至80%，成本仅为教师模型的30%，还可平衡市场供给。
AI 中文摘要
传统搜索系统被优化为检索与查询严格匹配的商品，通常优先考虑准确率而非召回率。在电商市场，尤其是杂货领域，这种范式存在局限性，因为用户满意度和商业结果在很大程度上取决于替代商品、互补商品和主题相关商品的可发现性。本文提出了一种用于发现增强型搜索的可扩展系统，该系统利用意图条件召回扩展。我们的方法生成隐式用户意图以扩展候选召回，同时保持相关性。该系统通过两阶段混合架构解决生成式检索的成本质量权衡问题：首先，我们利用闭源权重大语言模型（LLMs）为热门查询最大化可发现性；为将这些优势扩展到长尾查询，我们引入了一种微调的小语言模型（SLM），其通过LoRA适配器和师生蒸馏进行训练。我们使用严格的双重框架评估该系统：（a）针对语义质量的人工偏好验证的LLM作为评判指标，（b）端到端会话级购买分析。结果表明，我们的方法既提高了意图生成质量，又提高了下游检索有效性，将查询流量的发现覆盖率从约60%扩展到80%，推理成本约为教师模型的30%，为在大规模市场部署提供了可行路径。除了相关性提升外，发现增强型搜索还可作为市场平衡机制，为长尾和新兴供给提供按查询条件曝光的机会。
英文摘要
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.
CommentsAccepted to KDD 2026 TSMO
