Role-Augmented Intent-Driven Generative Search Engine Optimization

TL;DR

This paper introduces G-SEO, a structured optimization method for generative search engines that models search intent through role-based refinement, improving content visibility and alignment with user queries.

Contribution

It proposes a novel role-augmented intent-driven approach for SEO in generative search engines and enhances benchmarking with diversified datasets and a new evaluation rubric.

Findings

Search intent effectively guides content optimization in GSEs.

G-SEO significantly improves content visibility over baseline methods.

Enhanced evaluation metrics better reflect human-aligned assessment.

Abstract

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.