StructGEO: Intent-Guided Structural Organization for Generative Engine Optimization

Generative Engine Optimization (GEO) aims to rewrite a document so that generative engines are more likely to include its content in their responses. Recent studies infer potential user intents and then use them to guide rewriting. While effective, they have two limitations. First, because potential queries are inferred independently, they tend to concentrate on a few dominant search intents, resulting in insufficient coverage of less prominent information needs. Second, they focus on what evidence a document contains but overlook how that evidence is organized, even though organization affects whether a generative engine actually uses it. To address both issues, we propose StructGEO, a GEO framework that models search intents hierarchically and organizes supporting evidence to match. Specifically, StructGEO first constructs a top-down intent tree by recursively decomposing broad search intents into fine-grained ones. It then performs structure-aware rewriting by consolidating evidence relevant to similar intents into coherent sections. These steps broaden the coverage of information needs and keep related evidence coherent rather than scattered throughout the document. Extensive experiments show that StructGEO consistently outperforms existing GEO methods, achieving average gains of 21.2% and 17.5% over AutoGEO and IF-GEO, respectively, in Position-Adjusted Word Count (Word & Pos) visibility.