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.

翻译：广告与推荐系统中的点击率预测模型高度依赖物品ID嵌入，但在物品冷启动场景下表现不佳。本文提出IDProxy解决方案，利用多模态大语言模型从丰富的内容信号中生成代理嵌入，使得无需使用数据即可对新物品进行有效的CTR预测。这些代理嵌入与现有ID嵌入空间显式对齐，并在CTR目标下与排序模型进行端到端联合优化，从而能够无缝集成到现有的大规模排序流水线中。离线实验与在线A/B测试验证了IDProxy的有效性，该方案已成功部署于小红书发现页的内容流与展示广告业务，每日服务数亿用户。