IntentGEO 提出意图感知生成引擎优化，AMO 让引用排名平均提升 35%
IntentGEO: Intent-aware Generative Engine Optimization for Recommendation
研究发现自动优化提示反而可能越改越差，AMO 用固定核心指令加可学习附加策略的思路，把引用排名提升 35%，做 GEO 或推荐系统的可以看看。
论文 IntentGEO 研究如何在 LLM 生成的推荐结果中提升商品的可见度，并指出同一份商品描述在不同用户意图下的可见度收益差异很大。作者发现现有基于学习的提示优化方法平均可能不如手写种子提示，原因是提示在迭代中漂移成通用推销话术。为此提出 AMO（Anchored Meta-Optimization），将推荐指令固定为核心，仅从引用反馈学习附加策略。在 4 个领域、10 种意图状态下，AMO 相比最强的学习型基线将平均引用排名增益提高 35%，其中 9 种意图状态领先，并在智能体推荐流程和豆包上验证有效。
IntentGEO: Intent-aware Generative Engine Optimization for Recommendation
Large language models (LLMs) increasingly shape recommendations across chatbots, e-commerce, and user-generated content (UGC) platforms, creating demand for Generative Engine Optimization (GEO) to improve item visibility in generated responses. Existing GEO studies are mainly content-centric, optimizing visibility under general-purpose queries. However, a shared item description usually serves users with different needs and decision goals, resulting in its visibility gains being dependent on the request. To investigate how changing intent affects visibility, we formulate IntentGEO to evaluate shared rewrites across need specificity and decision progression. We revisit existing GEO methods and find that their visibility gains vary across user intents. More critically, learning-based prompt optimization can underperform its handwritten seed on average. Tracing these updates reveals prompt drift from explicit recommendation instructions toward generic promotional tactics. To address this drift, we propose AMO (Anchored Meta-Optimization), which preserves recommendation instructions in a fixed core while learning appended strategies from citation feedback. Across four domains and ten intent states, AMO improves average citation-rank gain by 35% over the strongest prior learning-based baseline and leads on nine states. Ablations show that learned additions improve on the fixed core alone and that AMO outperforms full-prompt optimization initialized from that core. It also achieves the highest gains in an agentic recommendation pipeline and, after separate adaptation, on Doubao, a commercially deployed, closed-source online assistant. These findings highlight user intent as a key dimension of GEO evaluation and support anchored adaptation across recommendation needs and decision stages.
