External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. However, with a larger model scale, such prior studies have a significantly increasing gap from industry as they often neglect two fundamental challenges in industrial-scale applications. First, training and inference budgets are restricted for the model to be served, exceeding which may incur latency and impair user experience. Second, large-volume data arrive in a streaming mode with data distributions dynamically shifting, as new users/ads join and existing users/ads leave the system. We propose the External Large Foundation Model (ExFM) framework to address the overlooked challenges. Specifically, we develop external distillation and a data augmentation system (DAS) to control the computational cost of training/inference while maintaining high performance. We design the teacher in a way like a foundation model (FM) that can serve multiple students as vertical models (VMs) to amortize its building cost. We propose Auxiliary Head and Student Adapter to mitigate the data distribution gap between FM and VMs caused by the streaming data issue. Comprehensive experiments on internal industrial-scale applications and public datasets demonstrate significant performance gain by ExFM.
Weilin Zhang、Xingliang Huang、Qianru Li、Shiquan Wang、Evelyn Lyu、Chunzhi Yang、Rui Zhang、Wenjun Wang、Jason Rudy、Mengyue Hang、Yinbin Ma、Kai Wang、Shuaiwen Wang、Sihan Zeng、Tongyi Tang、Xiaohan Wei、Longhao Jin、Jamey Zhang、Marcus Chen、Jiayi Xu、Angie Huang、Xihuan Zeng、Chi Zhang、Zhengli Zhao、Jared Yang、Qiang Jin、Xian Chen、Amit Anand Amlesahwaram、Lexi Song、Liang Luo、Yuchen Hao、Nan Xiao、Yavuz Yetim、Luoshang Pan、Gaoxiang Liu、Shuo Chang、Yuxi Hu、Yuzhen Huang、Jackie Xu、Rich Zhu、Mingfu Liang、Xi Liu、Rong Jin、Boyang Liu、Qiuling Suo、Qinghai Zhou、Song Zhou、Laming Chen、Hua Zheng、Zhiyuan Li、Shali Jiang、Jiyan Yang、Xiaozhen Xia、Fan Yang、Yasmine Badr、Ellie Wen、Shuyu Xu、Hansey Chen、Zhengyu Zhang、Jade Nie、Qin Huang、Chonglin Sun、Nancy Yu、Zhichen Zeng、Wenjing Lu、Xin Zhang、Yiqun Liu、Hang Yin、Yuxin Chen、Buyun Zhang、Xiaoyi Liu、Xingyuan Wang、Wenguang Mao、Zhijing Li、Zhehui Zhou、Feifan Gu、Shuo Gu、Ernest Wang、Shupin Mao、Benjamin Au、Jingzheng Qin、Peggy Yao、Jae-Woo Choi、Bin Gao、Yi Meng、Alex Gong、Edison Gao、Jack Hsueh、Musharaf Sultan、John Bocharov、Sagar Chordia、Xiaorui Gan、Peng Sun、Rocky Liu、Bo Long、Wenlin Chen、Santanu Kolay、Huayu Li、Lei Zhang、Wen-Yen Chen、Ted Lee、Yujie Zha、Jie Zheng、Alireza Vahdatpour、Yiping Han、Yantao Yao、Toshinari Kureha
计算技术、计算机技术
Weilin Zhang,Xingliang Huang,Qianru Li,Shiquan Wang,Evelyn Lyu,Chunzhi Yang,Rui Zhang,Wenjun Wang,Jason Rudy,Mengyue Hang,Yinbin Ma,Kai Wang,Shuaiwen Wang,Sihan Zeng,Tongyi Tang,Xiaohan Wei,Longhao Jin,Jamey Zhang,Marcus Chen,Jiayi Xu,Angie Huang,Xihuan Zeng,Chi Zhang,Zhengli Zhao,Jared Yang,Qiang Jin,Xian Chen,Amit Anand Amlesahwaram,Lexi Song,Liang Luo,Yuchen Hao,Nan Xiao,Yavuz Yetim,Luoshang Pan,Gaoxiang Liu,Shuo Chang,Yuxi Hu,Yuzhen Huang,Jackie Xu,Rich Zhu,Mingfu Liang,Xi Liu,Rong Jin,Boyang Liu,Qiuling Suo,Qinghai Zhou,Song Zhou,Laming Chen,Hua Zheng,Zhiyuan Li,Shali Jiang,Jiyan Yang,Xiaozhen Xia,Fan Yang,Yasmine Badr,Ellie Wen,Shuyu Xu,Hansey Chen,Zhengyu Zhang,Jade Nie,Qin Huang,Chonglin Sun,Nancy Yu,Zhichen Zeng,Wenjing Lu,Xin Zhang,Yiqun Liu,Hang Yin,Yuxin Chen,Buyun Zhang,Xiaoyi Liu,Xingyuan Wang,Wenguang Mao,Zhijing Li,Zhehui Zhou,Feifan Gu,Shuo Gu,Ernest Wang,Shupin Mao,Benjamin Au,Jingzheng Qin,Peggy Yao,Jae-Woo Choi,Bin Gao,Yi Meng,Alex Gong,Edison Gao,Jack Hsueh,Musharaf Sultan,John Bocharov,Sagar Chordia,Xiaorui Gan,Peng Sun,Rocky Liu,Bo Long,Wenlin Chen,Santanu Kolay,Huayu Li,Lei Zhang,Wen-Yen Chen,Ted Lee,Yujie Zha,Jie Zheng,Alireza Vahdatpour,Yiping Han,Yantao Yao,Toshinari Kureha.External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation[EB/OL].(2025-07-14)[2025-08-02].https://arxiv.org/abs/2502.17494.点此复制
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