Generative Search Engines (GSEs), powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), are reshaping information retrieval. While commercial systems (e.g., BingChat, Perplexity.ai) demonstrate impressive semantic synthesis capabilities, their black-box nature fundamentally undermines established Search Engine Optimization (SEO) practices. Content creators face a critical challenge: their optimization strategies, effective in traditional search engines, are misaligned with generative retrieval contexts, resulting in diminished visibility. To bridge this gap, we propose a Role-Augmented Intent-Driven Generative Search Engine Optimization (G-SEO) method, providing a structured optimization pathway tailored for GSE scenarios. Our method models search intent through reflective refinement across diverse informational roles, enabling targeted content enhancement. To better evaluate the method under realistic settings, we address the benchmarking limitations of prior work by: (1) extending the GEO dataset with diversified query variations reflecting real-world search scenarios and (2) introducing G-Eval 2.0, a 6-level LLM-augmented evaluation rubric for fine-grained human-aligned assessment. Experimental results demonstrate that search intent serves as an effective signal for guiding content optimization, yielding significant improvements over single-aspect baseline approaches in both subjective impressions and objective content visibility within GSE responses.

翻译：由大型语言模型（LLM）与检索增强生成（RAG）驱动的生成式搜索引擎（GSE）正在重塑信息检索范式。尽管商业系统（如BingChat、Perplexity.ai）展现了卓越的语义综合能力，但其黑箱特性从根本上动摇了既有的搜索引擎优化（SEO）实践。内容创作者面临关键挑战：在传统搜索引擎中有效的优化策略与生成式检索语境存在错位，导致内容可见度降低。为弥合这一鸿沟，我们提出角色增强型意图驱动的生成式搜索引擎优化（G-SEO）方法，为GSE场景提供结构化优化路径。该方法通过跨角色信息需求的反思性精炼建模搜索意图，实现靶向内容增强。为在真实场景中更好评估该方法，我们克服了先前研究的基准评估局限：（1）扩展GEO数据集，纳入反映真实搜索场景的多样化查询变体；（2）提出G-Eval 2.0——基于LLM增强的6级评估量规，实现细粒度的人类对齐评估。实验结果表明，搜索意图可作为指导内容优化的有效信号，在主观印象与GSE响应中的客观内容可见度两方面，均显著优于单维基线的优化方法。