Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior
arXiv:2603.29979v1 Announce Type: cross 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), a
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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.
Comments: 12 pages, 5 figures. This paper proposes GEO-SFE, a structural feature engineering framework for generative engine optimization
Subjects:
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
ACM classes: H.3.3; I.2.7
Cite as: arXiv:2603.29979 [cs.CL]
(or arXiv:2603.29979v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2603.29979
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: MuFeng Yang [view email] [v1] Tue, 31 Mar 2026 16:41:43 UTC (953 KB)
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