用大模型和实时购物车数据，提升生鲜与日用品的跨品类推荐效果。
Grocery to General Merchandise: A Cross-Pollination Recommender using LLMs and Real-Time Cart Context
- 结合共购分析与大模型挖掘商品间新关联。
- 物品页推荐加购率提升36%，购物车页提升15%。
- 适合做电商跨品类推荐的算法研究者与工程师。
现代电商平台致力于提供及时且情境相关的推荐服务。然而，如何向专注生鲜购物的用户推荐日用品（如牛奶搭配奶泡器）仍是关键但未被充分探索的挑战。本文提出一种跨品类推荐（XP）框架，通过多源商品关联和实时购物车上下文，弥合生鲜与日用品之间的推荐鸿沟。该方案采用两阶段架构：(1) 候选生成阶段，利用共购市场篮子分析与大模型方法识别新型商品-商品关联；(2) 排序阶段，采用基于Transformer的排序器，融合实时序列购物车上下文，并优化加购等参与度信号。离线分析与线上A/B测试表明，在物品页使用大模型检索可使加购率提升36%，在购物车页基于购物车上下文的排序器使加购率提升15%。本工作为跨品类推荐提供了实用技术，并为电商系统带来更广泛的洞察。
原文摘要 · Abstract (English)
Modern e-commerce platforms strive to enhance customer experience by providing timely and contextually relevant recommendations. However, recommending general merchandise to customers focused on grocery shopping -- such as pairing milk with a milk frother -- remains a critical yet under-explored challenge. This paper introduces a cross-pollination (XP) framework, a novel approach that bridges grocery and general merchandise cross-category recommendations by leveraging multi-source product associations and real-time cart context. Our solution employs a two-stage framework: (1) A candidate generation mechanism that uses co-purchase market basket analysis and LLM-based approach to identify novel item-item associations; and (2) a transformer-based ranker that leverages the real-time sequential cart context and optimizes for engagement signals such as add-to-carts. Offline analysis and online A/B tests show an increase of 36\% add-to-cart rate with LLM-based retrieval on the item page, and 15\% lift in add-to-cart using cart context-based ranker on the cart page. Our work contributes practical techniques for cross-category recommendations and broader insights for e-commerce systems.
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