发表机构
Adobe Inc.（奥多比公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
REALMS是一个基于LLM的对话系统，通过向量检索和NL2SQL实现高维画像数据的实时精确受众规模测算，显著降低延迟并提升可扩展性。
AI 中文摘要
受众规模测算是数字营销的关键组成部分。它能够实现精确的资源分配、活动规划和性能优化。传统方法使用骨架受众、抽样或预测建模，在处理高维画像数据时存在显著延迟、估计误差和可扩展性差的问题。我们提出了REALMS（基于LLM的多属性搜索的实时精确受众规模测算），一个在生产环境中部署于企业客户数据平台上的对话系统，用于精确受众规模测算。REALMS使营销人员能够使用自然语言查询包含数百万画像和数千属性的海量画像存储，并在数秒内获得精确计数。该系统引入了三个关键组件：（1）使用基于嵌入的向量搜索的分类属性检索机制，无需手动配置即可动态识别相关模式属性；（2）基于LLM的NL2SQL流水线，采用基于模板的上下文学习，在复杂嵌套模式上生成准确查询；（3）模式标准化，支持跨不同企业环境的行业无关部署。在真实企业数据上的评估表明，属性检索具有强召回率、高SQL执行准确性和低延迟，这使得实时交互式受众洞察成为可能，而先前方法需要数小时才能完成。
英文摘要
Audience sizing is a critical component of digital marketing. It enables precise resource allocation, campaign planning, and performance optimization. Traditional approaches using skeleton audiences, sampling, or predictive modeling suffer from significant delays, estimation errors, and poor scalability over high-dimensional profile data. We present REALMS (Real-time Exact Audience sizing via LLM-based Multi-attribute Search), a conversational system for exact audience sizing deployed in production on an enterprise customer data platform. REALMS enables marketers to query massive profile stores with millions of profiles and thousands of attributes using natural language and receive precise counts in seconds. The system introduces three key components: (1) a categorical attribute retrieval mechanism using embedding-based vector search to dynamically identify relevant schema attributes without manual configuration; (2) an LLM-powered NL2SQL pipeline with template-based in-context learning for accurate query generation over complex nested schemas; and (3) schema standardization enabling industry-agnostic deployment across diverse enterprise environments. Evaluation on real enterprise data demonstrates strong recall for attribute retrieval, high SQL execution accuracy, and low latency, which enables real-time interactive audience insights where prior methods required hours.
CommentsAccepted by ICDM 2026
