考虑竞争环境的生成优化策略组合，提升内容可见性
Beyond the Vacuum: Combinatorial Strategy Selection for Competitor-Aware Generative Engine Optimization
- 用贝叶斯优化搜索多种重写策略组合
- 在两个数据集上超越现有方法，多指标领先
- 可跨领域迁移，适合需要持续优化内容的场景
生成式引擎优化（GEO）作为一种新范式，通过改写内容提升其在大语言模型响应中的可见性。传统GEO方法独立选择重写策略，忽略了随着内容优化普及，最优策略会动态变化这一关键外部性。本文将GEO形式化为竞争感知的策略选择问题，提出两阶段方案：(1) 使用贝叶斯优化组合结构（BOCS）高效搜索重写策略空间；(2) 基于BOCS黑箱输出生成偏好对与基于依据的推理轨迹，微调语言模型以分析文档语料并推荐最优策略组合。在geo-bench和我们合成增强的竞争数据集geo-bench_comp上，该方法在多个曝光指标上达到当前最佳表现，优于现有代理型与单启发式方法。方法还成功迁移至多个分布外数据集，在不同领域、查询类型与文档类型中均有效。
原文摘要 · Abstract (English)
Generative Engine Optimization (GEO) has emerged as a novel paradigm for transforming content to increase visibility in Large Language Model (LLM) responses. Traditional GEO methods, however, select rewriting strategies in isolation, ignoring a critical externality: as adoption of content optimization grows, optimal strategies for rewriting content change. We formalize GEO as a competitor-aware strategy selection problem and propose a two-phase pipeline to solve it: (1) We use Bayesian Optimization of Combinatorial Structures (BOCS) to efficiently search the space of rewriting strategies, (2) We generate preference pairs and grounded reasoning traces from the BOCS black-box observations to fine-tune a language model to analyze a document corpus and propose optimal rewriting strategy combinations. We achieve state-of-the-art performance across several impression metrics over existing agentic and single-heuristic methods on both geo-bench and our synthetically augmented competitive dataset geo-bench_comp. Our method also transfers to multiple out-of-distribution datasets, proving effective across domains, queries, and document types.
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