---
id: 20260416-T0-19
title: "LLM-HYPER：基于大模型超网络的冷启动广告个性化生成"
title_en: "LLM-HYPER: LLM-Based Hypernetworks for Ad Personalization"
url: https://ai.daily.yangsir.net/daily/20260416-T0-19
issue_date: 2026-04-16
publish_date: 2026-04-15T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2604.12096
---

# LLM-HYPER：基于大模型超网络的冷启动广告个性化生成

arXiv新研究提出LLM-HYPER框架，解决在线广告平台中新广告的冷启动问题。传统方法缺乏足够的用户反馈数据进行模型训练，而LLM-HYPER将大语言模型作为超网络，生成个性化点击率预测模型。该方法特别适合新推广广告，在没有历史数据的情况下也能提供准确的个性化推荐。

## English Version

**LLM-HYPER: LLM-Based Hypernetworks for Ad Personalization**

New arXiv research proposes LLM-HYPER, a framework treating LLMs as hypernetworks to address the cold-start problem of new ads on online platforms. It generates personalized CTR prediction models without relying on historical user feedback data.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2604.12096)

**详情页**：https://ai.daily.yangsir.net/daily/20260416-T0-19

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