Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior

TL;DR

This paper introduces GEO-SFE, a framework for optimizing content structure at macro, meso, and micro levels to enhance citation probability and content visibility in AI-powered search engines.

Contribution

It systematically models structural features' impact on citation behavior and develops architecture-aware strategies for content optimization in generative engines.

Findings

Citation rate improved by 17.3% across six engines.

Subjective quality increased by 18.5%.

Structural optimization is validated as effective and generalizable.

Abstract

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.