生成引擎优化的结构特征工程：内容结构如何塑造引用行为
Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior
AI总结：
本文提出GEO-SFE框架，通过分解内容结构为三级层次，优化生成引擎中的结构特征，提升引用率和主观质量。
摘要：
人工智能驱动的搜索引擎普及使信息发现从传统链接检索转向直接答案生成并选择性引用来源，带来了新的挑战。现有生成引擎优化方法主要关注语义内容修改，而结构特征对引用行为的影响仍待探索。本文提出GEO-SFE系统框架，将内容结构分为宏观结构（文档架构）、中观结构（信息分块）和微观结构（视觉强调）三个层级，建模其对不同生成引擎架构下引用概率的影响。我们开发了架构感知的优化策略和预测模型，在保持语义完整性的同时提升结构有效性。在六个主流生成引擎上的实验评估显示，引用率提高了17.3%，主观质量提高了18.5%，验证了所提框架的有效性和通用性。本文将结构优化确立为生成引擎优化的基础组成部分，提供了一种数据驱动的方法来增强LLM信息生态系统中的内容可见性。
英文摘要：
The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
