LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System
Summary: LEADRE uses intent-aware prompts and multifaceted LLM knowledge to generate diverse ads, with auxiliary tasks and DPO aligning models to advertising objectives. Latency-aware hybrid serving scales the system to tens of billions of daily requests and improves online GMV. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Fengxin Li (Renmin University of China)
- 2. Yi Li (Tencent)
- 3. Yue Liu (Tencent)
- 4. Chao Zhou (Tencent)
- 5. Yuan Wang (Tencent)
- 6. Xiaoxiang Deng (Tencent)
- 7. Wei Xue (Tencent)
- 8. Dapeng Liu (Tencent)
- 9. Lei Xiao (Tencent)
- 10. Haijie Gu (Tencent)
- 11. Jie Jiang (Tencent)
- 12. Hongyan Liu (Tsinghua University)
- 13. Biao Qin (Renmin University of China)
- 14. Jun He (Renmin University of China)
BibTeX Citation
@article{li_vldb25,
title = {{LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System}},
author = {Li, Fengxin and Li, Yi and Liu, Yue and Zhou, Chao and Wang, Yuan and Deng, Xiaoxiang and Xue, Wei and Liu, Dapeng and Xiao, Lei and Gu, Haijie and Jiang, Jie and Liu, Hongyan and Qin, Biao and He, Jun},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {4763--4776},
doi = {10.14778/3750601.3750602},
url = {https://doi.org/10.14778/3750601.3750602},
year = {2025}
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