IDProxy: Cold-Start CTR Prediction for Ads and Recommendation at Xiaohongshu with Multimodal LLMs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910037772861440 |
|---|---|
| author | Zhang, Yubin Xu, Haiming Salha-Galvan, Guillaume Han, Ruiyan Xiao, Feiyang Huang, Yanhua Lin, Li Luo, Yang Hu, Yao |
| author_facet | Zhang, Yubin Xu, Haiming Salha-Galvan, Guillaume Han, Ruiyan Xiao, Feiyang Huang, Yanhua Lin, Li Luo, Yang Hu, Yao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01590 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IDProxy: Cold-Start CTR Prediction for Ads and Recommendation at Xiaohongshu with Multimodal LLMs Zhang, Yubin Xu, Haiming Salha-Galvan, Guillaume Han, Ruiyan Xiao, Feiyang Huang, Yanhua Lin, Li Luo, Yang Hu, Yao Information Retrieval Machine Learning 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. |
| title | IDProxy: Cold-Start CTR Prediction for Ads and Recommendation at Xiaohongshu with Multimodal LLMs |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2603.01590 |