用多模态大模型生成新商品点击率预测嵌入，解决冷启动难题。
IDProxy: Cold-Start CTR Prediction for Ads and Recommendation at Xiaohongshu with Multimodal LLMs
- 用多模态大模型从内容信号生成代理嵌入，替代缺失的物品ID嵌入。
- 在离线与线上测试中显著提升新物品点击率预测效果，支持大规模部署。
- 适合需要快速响应新内容推荐的平台，如社交电商、信息流广告。
广告与推荐系统中的点击率（CTR）模型高度依赖物品ID嵌入，但在物品冷启动场景下表现不佳。本文提出IDProxy，利用多模态大语言模型（MLLMs）从丰富的内容信号中生成代理嵌入，实现无使用数据的新物品有效CTR预测。这些代理嵌入被显式对齐至现有ID嵌入空间，并与排序模型端到端联合优化，可无缝集成至现有的大规模排序流水线。离线实验与在线A/B测试表明，IDProxy效果显著，已在小红书探索页的内容推荐与展示广告模块成功部署，日均服务数亿用户。
原文摘要 · Abstract (English)
Click-through rate (CTR) models in advertising and recommendation systems rely heavily on item ID embeddings, which struggle in item cold-start settings. We present IDProxy, a solution that leverages multimodal large language models (MLLMs) to generate proxy embeddings from rich content signals, enabling effective CTR prediction for new items without usage data. These proxies are explicitly aligned with the existing ID embedding space and are optimized end-to-end under CTR objectives together with the ranking model, allowing seamless integration into existing large-scale ranking pipelines. Offline experiments and online A/B tests demonstrate the effectiveness of IDProxy, which has been successfully deployed in both Content Feed and Display Ads features of Xiaohongshu's Explore Feed, serving hundreds of millions of users daily.
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