arXiv · Information Retrieval· Hua Zheng, Shali Jiang, Boyang Liu, Laming Chen, Kenny Lov, Chuanqi Xu, Lisang Ding, Qinghai Zhou, Can Cui, Xiaolong Liu, Xiaoyi Liu, Yasmine Badr, Xin Xu, Mingfu Liang, Jiyan Yang, Ellie Dingqiao Wen, Gerard Jonathan Mugisha Akkerhuis, Jason Rudy, Xi Liu, Chenxiao Guan, Rong Jin, Ruichao Qiu, Xian Chen, Zhehui Zhou, Ping Chen, Rui Yang, Haicheng Chen, Meet Raval, Song Zhou, Dharak Kharod, Shuyu Xu, Xingyuan Wang, Liang Tao, Qiang Jin, Qiao Yang, Wankun Zhu, Qin Huang, Yuzhen Huang, Darren Liu, Parish Aggarwal, Hui Zhou, Erzhuo Wang, Shuo Chang, Xiaorui Gan, Wenlin Chen, Santanu Kolay, Huayu Li·· 3 小时前AI 评分31
LoopFM：从基础模型历史表征中学习以提升推荐效果
LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation
AI 导读
LoopFM 是一种知识蒸馏框架，将基础模型（FM）的中间嵌入结构化为下游垂直模型（VM）的输入特征，开辟高带宽知识迁移通道，且无需在服务时实时运行 FM 推理或耦合 FM 与 VM 架构。
来源：arXiv · Information Retrieval · arxiv.org