Beyond Keywords: Driving Generative Search Engine Optimization with Content-Centric Agents
It addresses the urgent need for content optimization in generative search, providing a principled foundation for creators and researchers, though it is incremental in building on existing SEO concepts.
This paper tackles the problem of optimizing content for Generative Search Engines, where traditional SEO metrics are obsolete, by introducing an end-to-end framework that includes a benchmark and a multi-agent system, resulting in novel insights and actionable strategies for content creators.
The paradigm shift from traditional ranked-based search to Generative Search Engines has rendered conventional SEO metrics obsolete, creating an urgent need to understand, measure, and optimize for content influence on synthesized answers. This paper introduces a comprehensive, end-to-end framework for Generative Search Engine Optimization (GSEO) to address this challenge. We make two primary contributions. First, we construct CC-GSEO-Bench, a large-scale, content-centric benchmark, and propose a multi-dimensional evaluation framework that systematically quantifies influence, moving beyond surface-level attribution to assess substantive semantic impact. Second, we design a novel multi-agent system that operationalizes this framework, automating the strategic refinement of content through a collaborative analyze-revise-evaluate workflow. Our empirical analysis using this framework reveals novel insights into the dynamics of content influence, offering actionable strategies for creators and establishing a principled foundation for future GSEO research.