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[2602.22732] Generative Recommendation for Large-Scale Advertising
Summary
This paper introduces GR4AD, a generative recommendation system designed for large-scale advertising, enhancing ad revenue through innovative architecture and learning techniques.
Why It Matters
As digital advertising grows, effective recommendation systems are crucial for maximizing ad revenue. GR4AD's advancements in generative recommendation can significantly improve performance in real-time applications, making it relevant for businesses seeking to optimize their advertising strategies.
Key Takeaways
- GR4AD integrates architecture, learning, and serving for effective generative recommendations.
- The system employs innovative techniques like UA-SID and LazyAR to enhance performance and reduce costs.
- Dynamic beam serving adapts to real-time conditions, optimizing compute resources.
- A/B testing shows a 4.2% increase in ad revenue compared to traditional methods.
- GR4AD has been successfully deployed in a large-scale advertising system with over 400 million users.
Computer Science > Information Retrieval arXiv:2602.22732 (cs) [Submitted on 26 Feb 2026] Title:Generative Recommendation for Large-Scale Advertising Authors:Ben Xue, Dan Liu, Lixiang Wang, Mingjie Sun, Peng Wang, Pengfei Zhang, Shaoyun Shi, Tianyu Xu, Yunhao Sha, Zhiqiang Liu, Bo Kong, Bo Wang, Hang Yang, Jieting Xue, Junhao Wang, Shengyu Wang, Shuping Hui, Wencai Ye, Xiao Lin, Yongzhi Li, Yuhang Chen, Zhihui Yin, Quan Chen, Shiyang Wen, Wenjin Wu, Han Li, Guorui Zhou, Changcheng Li, Peng Jiang View a PDF of the paper titled Generative Recommendation for Large-Scale Advertising, by Ben Xue and 28 other authors View PDF HTML (experimental) Abstract:Generative recommendation has recently attracted widespread attention in industry due to its potential for scaling and stronger model capacity. However, deploying real-time generative recommendation in large-scale advertising requires designs beyond large-language-model (LLM)-style training and serving recipes. We present a production-oriented generative recommender co-designed across architecture, learning, and serving, named GR4AD (Generative Recommendation for ADdvertising). As for tokenization, GR4AD proposes UA-SID (Unified Advertisement Semantic ID) to capture complicated business information. Furthermore, GR4AD introduces LazyAR, a lazy autoregressive decoder that relaxes layer-wise dependencies for short, multi-candidate generation, preserving effectiveness while reducing inference cost, which facilitates scaling under f...
