用大模型生成商业意图，实时高效召回广告
Real-time Ad retrieval via LLM-generative Commercial Intention for Sponsored Search Advertising
- 用大模型生成商业意图文本作为中间表示，替代传统ID映射
- 线上实测带来5.04%消费增长、6.37%GMV提升和1.28%点击率改善
- 适合大规模广告系统，尤其看重实时性与可扩展性的场景
将大语言模型（LLM）与检索系统结合，在广告检索中展现出巨大潜力。现有方法通过生成数值或内容型文档ID来召回广告，但存在数值ID与文档的单对多映射问题，且内容提取耗时，导致语义效率低，难以在大规模语料中扩展。本文提出实时广告检索框架RARE，利用定制化大模型生成的商业意图（CIs）作为中间语义表征，直接实现查询到广告的实时检索。这些商业意图由注入商业知识的模型生成，提升了领域相关性，且每个意图对应多个广告，形成轻量且可扩展的集合。RARE已在真实在线系统中部署，日均处理搜索量达数亿级。线上实验显示：消费提升5.04%，GMV增长6.37%，点击率提高1.28%，浅层转化率上升5.29%。离线实验表明，RARE在四大类共十种基线方法中表现更优。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) with retrieval systems has shown promising potential in retrieving documents (docs) or advertisements (ads) for a given query. Existing LLM-based retrieval methods generate numeric or content-based DocIDs to retrieve docs/ads. However, the one-to-few mapping between numeric IDs and docs, along with the time-consuming content extraction, leads to semantic inefficiency and limits scalability in large-scale corpora. In this paper, we propose the Real-time Ad REtrieval (RARE) framework, which leverages LLM-generated text called Commercial Intentions (CIs) as an intermediate semantic representation to directly retrieve ads for queries in real-time. These CIs are generated by a customized LLM injected with commercial knowledge, enhancing its domain relevance. Each CI corresponds to multiple ads, yielding a lightweight and scalable set of CIs. RARE has been implemented in a real-world online system, handling daily search volumes in the hundreds of millions. The online implementation has yielded significant benefits: a 5.04% increase in consumption, a 6.37% rise in Gross Merchandise Volume (GMV), a 1.28% enhancement in click-through rate (CTR) and a 5.29% increase in shallow conversions. Extensive offline experiments show RARE's superiority over ten competitive baselines in four major categories.
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