构建百万级买家画像数据集，用于分析生成式引擎的推荐逻辑。
Demand-Side Measurement for Generative Engine Optimization: Constructing and Validating a Million-Persona, Intent-Annotated Buyer Corpus
- 用合成数据+去重技术生成103万买家画像，覆盖511个行业和4个市场场景。
- 每条画像含搜索查询、意图标签（信息/商业/交易）及信任来源偏好。
- 专为研究生成式引擎如何选品设计，适合广告与推荐系统研究者使用。
生成式引擎如ChatGPT、Gemini和Perplexity可直接回答买家问题并列出品牌短名单。要研究品牌如何进入或未能进入该短名单，需需求端数据：买家在品类中问什么、需要何种信息、信任哪些来源。现有大规模人物画像数据集主要用于训练数据多样性，缺乏分阶段的搜索意图标签和首选来源字段，无法与供给端推荐结果关联。本文构建并验证了PersonaGen-1M，包含1,031,732条合成买家画像，覆盖511个行业标签和4个市场情境，共19,416,821个结构化行为属性，其中5,160,046条为搜索查询。每条画像带有单一主意图标签（78.3%信息性，17.4%商业性，4.3%交易性），以及一个标注买家信任来源类型的首选来源字段。数据源自约4000万条原始人物描述，经GPU加速的MinHash LSH与语义去重处理，后统一到固定模式。意图字段筛选出驱动推荐的商业评估型画像，首选来源字段可与引用溯源数据配对——此关联是主要用途，其控制性实证估计为未来工作。截至2026年8月，百万级画像数据集中仅有一份具备来源偏好属性，且仅为六值媒体渠道枚举；而PersonaGen-1M则为每条画像提供具体来源列表、分阶段意图标签与完整查询集合。完整数据集可申请非商业研究使用，分层子集已公开发布，供协议、模式与验证方法复用。
原文摘要 · Abstract (English)
Generative engines such as ChatGPT, Gemini, and Perplexity answer buyer questions directly and name a shortlist of brands inside the answer. Studying how brands enter or fail to enter that shortlist requires demand-side data: what buyers in a category ask, what information they need, and which sources they trust. Existing large persona corpora are built for training-data diversity and carry neither a staged search-intent label nor a preferred-sources field, so they cannot be joined to supply-side recommendation measurements. We built and validated PersonaGen-1M, a corpus of 1,031,732 synthetic buyer personas spanning 511 industry labels and 4 market contexts, carrying 19,416,821 structured behavioral attributes, 5,160,046 of them search queries. Each persona carries a single primary_intent label covering its query set (78.3% informational, 17.4% commercial, 4.3% transactional) and a preferred_sources field naming the source types that buyer would trust. The corpus was built from roughly 40 million raw persona descriptions drawn from four public datasets through GPU-accelerated MinHash LSH plus semantic deduplication, then enriched to a fixed schema. The intent field selects the commercial-evaluation personas whose queries drive recommendation, and the preferred_sources field pairs against citation-provenance data; that join is the primary intended use, and its controlled empirical estimate is future work. Among million-scale persona corpora surveyed in August 2026, one other carries a source-preference attribute, as a six-value media-channel enum; PersonaGen-1M pairs named per-persona source lists with a staged commercial search-intent label and an attached query set. The full corpus is shared on request for non-commercial research; a stratified subset is published openly so the protocol, the schema and the validation can be inspected and reused without asking us.
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