Role-Augmented Intent-Driven Generative Search Engine Optimization
Xiaolu Chen, Haojie Wu, Jie Bao, Zhen Chen, Yong Liao, Hu Huang

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.…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Web Data Mining and Analysis · Consumer Market Behavior and Pricing
