用大模型直接生成广告点击率参数，解决新广告冷启动难题
LLM-HYPER: Generative CTR Modeling for Cold-Start Ad Personalization via LLM-Based Hypernetworks
- 用大模型作为超网络，根据广告内容生成点击率预测权重
- 离线测试在NDCG@10上比基线提升55.9%，线上测试显著缩短冷启动期
- 适合需要快速部署新广告的电商平台，尤其关注冷启动场景
在线广告平台中，新上线的广告因缺乏用户反馈而面临冷启动问题。本文提出LLM-HYPER框架，将大语言模型（LLM）作为超网络，以无训练方式直接生成点击率（CTR）估计器的参数。该方法基于多模态广告内容（文本与图像）进行少样本思维链提示，推断线性CTR预测器的特征权重。通过CLIP嵌入检索语义相似的历史投放活动，并转化为提示示例，使LLM能够推理用户意图、特征影响与内容相关性。为保障数值稳定性和生产可用性，引入归一化与校准技术，使生成权重匹配实际生产中的CTR分布。大量离线实验表明，LLM-HYPER在NDCG@10上较冷启动基线提升55.9%。在美国顶级电商平台的线上A/B测试验证了其有效性，显著缩短冷启动周期并达到竞争力表现。该方案已成功投入生产。
原文摘要 · Abstract (English)
On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack sufficient user feedback for model training. In this work, we propose LLM-HYPER, a novel framework that treats large language models (LLMs) as hypernetworks to directly generate the parameters of the click-through rate (CTR) estimator in a training-free manner. LLM-HYPER uses few-shot Chain-of-Thought prompting over multimodal ad content (text and images) to infer feature-wise model weights for a linear CTR predictor. By retrieving semantically similar past campaigns via CLIP embeddings and formatting them into prompt-based demonstrations, the LLM learns to reason about customer intent, feature influence, and content relevance. To ensure numerical stability and serviceability, we introduce normalization and calibration techniques that align the generated weights with production-ready CTR distributions. Extensive offline experiments show that LLM-HYPER significantly outperforms cold-start baselines in NDCG$@10$ by 55.9\%. Our real-world online A/B test on one of the top e-commerce platforms in the U.S. demonstrates the strong performance of LLM-HYPER, which drastically reduces the cold-start period and achieves competitive performance. LLM-HYPER has been successfully deployed in production.
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