查询隐含的生成引擎优化

中文摘要 AI 辅助

搜索领域随着人们在线查找信息方式的改变而发生了巨大变化。传统搜索引擎正被生成式搜索引擎（GSEs）所取代，后者利用大型语言模型（LLMs）对用户查询生成自然语言响应。对于内容创作者而言，可见性不再仅仅由搜索结果中的排名决定，而是取决于其内容是否在生成的响应中被引用。然而，生成式搜索引擎是黑盒系统，这导致了生成引擎优化（GEO）的出现，这是一套旨在提高内容在生成式搜索环境中可见性的技术。大多数现有方法依赖于显式查询或从查询中派生的信号来调整内容，以更好地满足用户需求。我们提出了查询隐含的生成引擎优化（QI-GEO），该方法直接从文档中推断用户意图。我们的方法近似文档的意图空间，并识别可能缺失但与回答潜在用户查询相关的内容。在GEO-Bench和扩展GEO-Bench上的评估显示，在客观和主观指标上均有改进。QI-GEO将客观分数提高了最多15.9%，主观分数提高了最多17.6%，同时获得的引用增益几乎是引用损失的近两倍。这些结果表明，从文档中推导出的用户意图近似值可以在不依赖显式查询输入的情况下提高可见性。

英文摘要

The landscape of search has changed drastically with how people look for information online. Traditional search engines are being replaced by Generative Search Engines (GSEs), which use Large Language Models (LLMs) to generate natural language responses to user queries. For content creators, visibility is no longer solely determined by ranking in search results but by being cited within generated responses. But Generative Search Engines are black-boxes, leading to the emergence of Generative Engine Optimization (GEO), a set of techniques aimed at improving content visibility in generative search settings. Most existing approaches rely on the explicit queries or query derived signals to align content to better suit user needs. We propose Query Implied Generative Engine Optimization (QI-GEO) to infers user intent directly from the document. Our approach approximates document's intent space and identifies content that may be missing yet relevant to answer potential user queries. Evaluation on GEO-Bench and Extended GEO-Bench demonstrated improvements across objective and subjective metrics. QI-GEO improved objective scores by up to 15.9% and subjective scores by up to 17.6%, while yielding nearly twice as many citation gains as citation losses. These results suggest that document-derived approximations of user intents can improve visibility without relying on explicit query inputs.

发表机构

• San Jose State University（圣何塞州立大学）

机构由 AI 辅助整理，请以论文原文为准。